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Semantic Web Services:
Theory, Tools, and Applications
Jorge Cardoso
University of Madeira, Portugal
Hershey • New York
Information Science Reference

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Library of Congress Cataloging-in-Publication Data
Semantic Web services : theory, tools and applications / Jorge Cardoso, editor.
p. cm.
Summary: "This book brings together researchers, scientists, and representatives from different communities to study, understand,
and explore the theory, tools, and applications of the semantic Web. It joins the semantic Web, ontologies, knowledge management,
Web services, and Web processes into one fully comprehensive resource, serving as the platform for exchange of both practical
technologies and research"--Provided by publisher.
Includes bibliographical references and index.
ISBN 978-1-59904-045-5 (hardcover) -- ISBN 978-1-59904-047-9 (ebook)
1. Semantic Web. 2. Web services. I. Cardoso, Jorge, 1970-
TK5105.88815.S45 2006
025.04--dc22
2006033762
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book set is new, previously-unpublished material. The views expressed in this book are those of the au-
thors, but not necessarily of the publisher.

Table of Contents
Detailed Table of Contents ................................................................................................................... v
Foreword .
............................................................................................................................................viii
Preface.
.................................................................................................................................................... x
Acknowledgments .............................................................................................................................xvii
Chapter I
The Syntactic and the Semantic Web/ Jorge Cardoso............................................................................. 1
Chapter II
Logics for the Semantic Web/ Jos de Bruijn.
......................................................................................... 24
Chapter III
Ontological Engineering: What are Ontologies and How Can We Build Them?
Oscar Corcho, Mariano Fernández-López, and Asunción Gómez-Pérez.
............................................. 44
Chapter IV
Editing Tools for Ontology Creation/ Ana Lisete Nunes Escórcio and Jorge Cardoso.
........................ 71
Chapter V
Web Ontology Languages/ Grigoris Antoniou and Martin Doerr........................................................ 96
Chapter VI
Reasoning on the Semantic Web/ Rogier Brussee and Stanislav Pokraev.......................................... 110
Chapter VII
Introduction to Web Services/ Cary Pennington, Jorge Cardoso, John A. Miller,
Richard Scott Patterson, and Ivan Vasquez ........................................................................................ 134
Chapter VIII
Service-Oriented Processes: An Introduction to BPEL/ Chun Ouyang, Wil M.P. van der Aalst,
Marlon Dumas, Arthur H.M. ter Hofstede, and Marcello La Rosa .................................................... 155

Chapter IX
Semantic Web Services/ Rama Akkiraju.............................................................................................. 191
Chapter X
The Process of Semantic Annotation of Web Services/ Christoph Ringelstein,
Thomas Franz, and Steffen Staab ....................................................................................................... 217
Chapter XI
Semantic Web Service Discovery: Methods, Algorithms and Tools/ Vassileios Tsetsos,
Christos Anagnostopoulos, and Stathes Hadjiefthymiades................................................................. 240
Chapter XII
Semantic Web Service Discovery in the WSMO Framework/ Uwe Keller, Rubén Lara,
Holger Lausen, and Dieter Fensel....................................................................................................... 281
Chapter XIII
Semantic Search Engines Based on Data Integration Systems/ Domenico Beneventano
and Sonia Bergamaschi ...................................................................................................................... 317
About the Authors ............................................................................................................................. 343
Index.................................................................................................................................................... 350

Foreword .
............................................................................................................................................viii
Preface.
.................................................................................................................................................... x
Acknowledgments .............................................................................................................................xvii
Chapter I
The Syntactic and the Semantic Web/ Jorge Cardoso............................................................................. 1
This chapter gives an overview of the evolution of the Web. Initially, Web pages were specified syn-
tactically and were intended only for human consumption. New Internet business models, such as B2B
and B2C, require information on the Web to be defined semantically in a way that it can be used by
computers, not only for display purposes, but also for interoperability and integration. To achieve this
new type of Web, called Semantic Web, several technologies are being developed, such as the resource
description framework and the Web Ontology Language.
Chapter II
Logics for the Semantic Web/ Jos de Bruijn.
......................................................................................... 24
This chapter introduces several formal logical languages which form the backbone of the Semantic
Web. The basis for all these languages is the classical first-order logic. Some of the languages presented
include description logics, frame logic and RuleML.
Chapter III
Ontological Engineering: What are Ontolgies and How Can We Build Them?
Oscar Corcho, Mariano Fernández-López, and Asunción Gómez-Pérez.
............................................. 44
The term “ontological engineering” defines the set of activities that concern the ontology development
process, the ontology life cycle, the principles, methods and methodologies for building ontologies, and
the tool suites and languages that support them. In this chapter we provide an overview of ontological
engineering, describing the current trends, issues and problems.
Detailed Table of Contents

Chapter IV
Editing Tools for Ontology Creation/ Ana Lisete Nunes Escórcio and Jorge Cardoso.
........................ 71
The activities associated with Ontological Engineering require dedicated tools. One of the first activities
is to find a suitable ontology editor. In this chapter we give an overview of the editing tools we consider
more relevant for ontology construction.
Chapter V
Web Ontology Languages/ Grigoris Antoniou and Martin Doerr........................................................ 96
This chapter gives a general introduction to some of the ontology languages that play an important role
on the Semantic Web. The languages presented include RDFS and OWL.
Chapter VI
Reasoning in the Semantic Web/ Rogier Brussee and Stanislav Pokraev........................................... 110
In this chapter we remember the reader the fundamental of description logic and the OWL ontology
language and explain how reasoning can be achieved on the Semantic Web.Areal example using routers
is given to explain how ontologies and reasoning can help in determining the location of resources.
Chapter VII
Introduction to Web Services/ Cary Pennington, Jorge Cardoso, John A. Miller,
Richard Scott Patterson, and Ivan Vasquez ........................................................................................ 134
This chapter reviews the history out of which Web services evolved. We will see that Web services are
the result of the evolution of several distributed systems technologies. One of the concepts introduced
along Web services is service-oriented architecture (SOA). Since SOA is to be used by organizations,
we address important issues such as the specification of policies and security.
Chapter VIII
Service-Oriented Processes: An Introduction to BPEL/ Chun Ouyang,
Wil M.P. van der Aalst, Marlon Dumas, Arthur H.M. ter Hofstede, and Marcello La Rosa .............. 155
The Business Process Execution Language for Web Services (BPEL) is an emerging standard for speci-
fying a business process made of Web services. In this chapter, we review some limitations of BPEL
and discuss solutions to address them. We also consider the possibility of applying formal methods and
Semantic Web technologies to support the development of a next generation of BPEL processes.
Chapter IX
Semantic Web Services/ Rama Akkiraju.............................................................................................. 191
Several researchers have recognized that Web services standards lack of semantics. To address this
limitation, the Semantic Web community has introduced the concept of Semantic Web service. When
the requirements and capabilities of Web services are described using semantics it becomes possible

to carry out a considerable number of automated tasks, such as automatic discovery, composition and
integration of software components.
Chapter X
The Process of Semantic Annotation of Web Services/ Christoph Ringelstein,
Thomas Franz, and Steffen Staab ....................................................................................................... 217
This chapter explains how Web services can be annotated and described with semantics. Semantic de-
scriptions allow Web services to be understood and correctly interpreted by machines. The focus lies in
analyzing the process of semantic annotation, i.e., the process of deriving semantic descriptions from
lower level specifications, implementations and contextual descriptions of Web services.
Chapter XI
Semantic Web Service Discovery: Methods, Algorithms, and Tools/ Vassileios Tsetsos,
Christos Anagnostopoulos, and Stathes Hadjiefthymiades................................................................. 240
This chapter surveys existing approaches to Semantic Web service discovery. Semantic discovery will
probably substitute existing keyword-based solutions in order to overcome several limitations of the
latter.
Chapter XII
Semantic Web Service Discovery in the WSMO Framework/ Uwe Keller, Rubén Lara,
Holger Lausen, and Dieter Fensel....................................................................................................... 281
This chapter presents how the Web service modeling ontology (WSMO) can be applied for service
discovery. WSMO is a specification that provides a conceptual framework for semantically describing
Web services and their specific properties. This chapter is closely related to Chapter XI.
Chapter XIII
Semantic Search Engines Based on Data Integration Systems/ Domenico Beneventano
and Sonia Bergamaschi ...................................................................................................................... 317
Syntactic search engines, such as Google and Yahoo!, are common tools for every user of the Internet.
But since the search is based only on the syntax of keywords, the accuracy of the engines is often poor
and inadequate. One solution to improve these engines is to add semantics to the search process. This
chapter presents the concept of semantic search engines which fundamentally augment and improve
traditional Web search engines by using not just words, but concepts and logical relationships.
About the Authors ............................................................................................................................. 343
Index.................................................................................................................................................... 350

viii
Semantic Web is here to stay! This is not really a marketing campaign logo, but it is a truth that every
year is becoming more and more relevant to the daily life of business world, industry and society.
I do not know how it happened, but the last years, through our activities in the Special Interest Group
on Semantic Web and Information Systems in the Association for Information Systems (http://www.
sigsemis.org), I had the opportunity to contact and collaborate with several key people for the evolution
of the SW as well as many leaders in different domains trying to understand their attitude for Semantic
Web1
. I feel many times my background in Informatics and Management Science helps me to go beyond
the traditional exhaustive technical discussions on Semantic Web and to see the Forest. This is full of
fertile grounds, fruits for the people who will put the required tough efforts for the cultivation of the
fields and many more, and of course much more value for the early adopters.
A couple years ago I had an interview with Robert Zmud, professor, and Michael F. Price, chair in
MIS, University of Oklahoma. Given his legendary work in the adoption of technologies in business/or-
ganizational contexts, I asked him in a way how can we promote Semantic Web to business world. His
answer influenced all of my Semantic Web activities until then. I am copying here:
As with all adoption situations, this is an information and communication problem. One needs to seg-
ment the base of potential adopters (both in the IS community and in the business community) and then
develop communication programs to inform each distinct segment of, first, the existence of the innova-
tion (know-what), then the nature of the innovation (know-how), and finally why this innovation would
be useful to them (know-why). These adopter segments are likely to be very different from each other.
Each will have a different likelihood of adoption and will likely require that a somewhat unique com-
munication strategy be devised and directed toward the segment
So this is why Jorge’s current edition, as well as planned editions, give an answer to the problem of
many people. Semantic Web is discussed in the triptych know-what, know-how, and know-why and the
editing strategy of the book boosts the excellent quality of well known contributors. It is really amazing
how Jorge made it and so many academics and practitioners collaboratively worked for this edition.
Robert Zmud concluded his answer with one more statement which is worthy to mention.
Myadvicethus,istosegmenttheadopterpopulation,identifythosecommunitieswiththehighestpotential
for adoption, develop a targeted communication strategy, and then develop the relationships necessary
to deliver the communication strategy. Hope this helps.
This answer really justifies why you are fortunate to read this book. Semantics are evident everywhere
in every aspect of business, life, and society (Sheth, 2005)1
. In this sense, “Semantic Web Services:
Theory, Tools, and Applications” provides a critical step forward in the understanding of the state of
the art of the Semantic Web.
Foreword

ix
I am convinced that the next years Semantic Web will drive a new era of real world applications.
With its transparent capacity to support every business domain, the milestone of the knowledge society
will be for sure a Semantic Web primer. Within this context, computer science and information systems
experts have to reconsider their role. They must be able to transform business requirements to systems
and solutions that go beyond traditional analysis and design. This is why a lot of effort must be paid to
the introduction of Semantic Web in computer science and information systems curricula. “Semantic
Web: Theory, Tools, and Applications” can be used as an excellent text book for the relevant themes.
As a concluding remark I would like just to share with you some thoughts. There is always a ques-
tioning for the pace of the change, and the current stage in the evolution of the SW. I do believe that
there is no need to make predictions for the future. The only thing we need is strategy and hard work.
Educating people in Semantic Web in computer science departments and in business schools means
making them realize that semantics, logic, reasoning, and trust are just our mankind characteristics that
we must bring to our “electronic words.” If we do not support them our virtual information world looks
like a giant with glass legs. This is why I like the engineering approach of Jorge in this edition. We must
be able to support the giant with concrete computer engineering in order to make sustainable solutions
for real world problems. The fine grain of strategy and computer science will lead Semantic Web to a
maturity level for unforeseen value diffusion.
My invitation is to be part of this exciting new journey and to keep in mind that the people who
dedicate their lives in the promotion of disciplines for the common wealth from time to time need en-
couragement and support because their intellectual work is not valued in financial terms. This is why I
want to express my deepest appreciation and respect for Jorge Cardoso as scientist and man, and to wish
him to keep rocking in Semantic Web.
Dear Jorge, you did once again great job. And dear Readers, from all over the world you did the
best choice. Let us open together the Semantic Web to the society. And why not let us put together the
new milestones towards a better world for all through the adoption of leading edge technologies in
humanistic visions.
Miltiadis D. Lytras, University of Patras, Greece
Endnotes
1
Sheth,A., Ramakrishnan C., & Thomas, C. (2005). Semantics for the Semantic Web: The implicit,
the formal and the powerful. International Journal on Semantic Web and Information Systems,
Inaugural Issue, 1(1), 1-18.
2
Lytras, M. (2005). Semantic Web and information systems: An agenda based on discourse with
community leaders. International Journal on Semantic Web and Information Systems, Inaugural
Issue, 1(1), i-xii.

What is This Book About?
The current World Wide Web is syntactic and the content itself is only readable by humans. The Seman-
tic Web proposes the mark-up of content on the Web using formal ontologies that structure underlying
data for the purpose of comprehensive machine understanding. Currently most Web resources can only
be found and queried by syntactical search engines. One of the goals of the Semantic Web is to enable
reasoning about data entities on different Web pages or Web resources. The Semantic Web is an exten-
sion of the current Web in which information is given well-defined meaning, enabling computers and
people to work in co-operation.
Along with the Semantic Web, systems and infrastructures are currently being developed to support
Web services. The main idea is to encapsulate an organization’s functionality within an appropriate
interface and advertise it as Web services. While in some cases Web services may be utilized in an
isolated form, it is normal to expect Web services to be integrated as part of Web processes. There is
a growing consensus that Web services alone will not be sufficient to develop valuable Web processes
due the degree of heterogeneity, autonomy, and distribution of the Web. Several researchers agree that
it is essential for Web services to be machine understandable in order to support all the phases of the
lifecycle of Web processes.
It is therefore indispensable to interrelate and associate two of the hottest RD and technology areas
currently associated with the Web—Web services and the Semantic Web. The study of the application
of semantics to each of the steps in the Semantic Web process lifecycle can help address critical issues
in reuse, integration and scalability.
Why Did I Put a Lot of Effort in Creating This Book?
I started using Semantic Web technologies in 2001 right after Tim Berners-Lee, James Hendler, and Ora
Lassila published their article entitled “The Semantic Web” in the May issue of Scientific American.
This seminal article described some of the future potential of what was called the Semantic Web, the
impact of computers understanding and interpreting semantic information, and how searches could be
dramatically improved when using semantic metadata. In 2004, I started planning to teach a course on
Semantic Web at the University of Madeira (Portugal). When looking for material and textbooks on the
topic for my students, I realized that there was only a hand full of good books discussing the concepts
associated with the Semantic Web. But none aggregated in one place the theory, the tools, and the ap-
plications of the Semantic Web. So, I decided to write this comprehensive and handy book for students,
teachers, and researchers.
The major goal of this book is to bring contributions from researchers, scientists from both industry
andacademics,andrepresentativesfromdifferentcommunitiestogethertostudy,understand,andexplore
the theory, tools and applications of the Semantic Web. It brings together computing that deal with the
Preface

xi
design and integration, bio-informatics, education, and so forth ontological engineering is defined as the
set of activities that concern the ontology development process, the ontology life cycle, the principles,
methods and methodologies for building ontologies, and the tool suites and languages that support them.
In Chapter III we provide an overview of all these activities, describing the current trends, issues and
problems. More specifically, we cover the following aspects of ontological engineering: (a) Methods
and methodologies for ontology development. We cover both comprehensive methodologies that give
support to a large number of tasks of the ontology development process and methods and techniques
that focus on specific activities of this process, focusing on: ontology learning, ontology alignment and
merge, ontology evolution and versioning, and ontology evaluation; (b) Tools for ontology develop-
ment. We describe the most relevant ontology development tools, which give support to most of the
ontology development tasks (especially formalization and implementation) and tools that have been
created for specific tasks, such as the ones identified before: learning, alignment and merge, evolution
and versioning and evaluation, and (c) finally, we describe the languages that can be used in the context
of the Semantic Web. This includes W3C recommendations, such as RDF, RDF schema and OWL, and
emerging languages, such as WSML.
Chapter IV gives an overview of editing tools for building ontologies. The construction of an ontol-
ogy demands the use of specialized software tools. Therefore, we give a synopsis of the tools that we
consider more relevant. The tools we have selected were Protégé, OntoEdit, DOE, IsaViz, Ontolingua,
Altova Semantic Works, OilEd, WebODE, pOWL and SWOOP. We started by describing each tool and
identifying which tools supported a methodology or other important features for ontology construction.
It is possible to identify some general distinctive features for each software tool. Protégé is used for
domain modeling and for building knowledge-base systems and promotes interoperability. DOE allows
users to build ontologies according to the methodology proposed by Bruno Bachimont. Ontolingua was
built to ease the development of ontologies with a form-based Web interface. Altova SemanticWorks
is a commercial visual editor that has an intuitive visual interface and drag-and-drop functionalities.
OilEd’s interface was strongly influenced by Stanford’s Protégé toolkit. This editor does not provide
a full ontology development environment. However, it allows users to build ontologies and to check
ontologies for consistency by using the FaCT reasoner. WebODE is a Web application. This editor sup-
ports ontology edition, navigation, documentation, merge, reasoning and other activities involved in the
ontology development process. pOWL is capable of supporting parsing, storing, querying, manipula-
tion, versioning and serialization of RDFS and OWL knowledge bases in a collaborative Web enabled
environment. SWOOP is a Web-based OWL ontology editor and browser. SWOOP contains OWL
validation and offers various OWL presentation syntax views. It has reasoning support and provides a
multiple ontology environment.
The aim of Chapter V is to give a general introduction to some of the ontology languages that play
a prominent role on the Semantic Web. In particular, it will explain the role of ontologies on the Web,
review the current standards of RDFS and OWL, and discuss open issues for further developments. In
the context of the Web, ontologies can be used to formulate a shared understanding of a domain in order
deal with differences in terminology of users, communities, disciplines and languages as it appears in
texts. One of the goals of the Semantic Web initiative is to advance the state of the current Web through
the use of semantics. More specifically, it proposes to use semantic annotations to describe the meaning
of certain parts of Web information and, increasingly, the meaning of message elements employed by
Web services. For example, the Web site of a hotel could be suitably annotated to distinguish between
the hotel name, location, category, number of rooms, available services and so forth Such meta-data
could facilitate the automated processing of the information on the Web site, thus making it accessible
to machines and not primarily to human users, as it is the case today. The current and most prominent
Web standard for semantic annotations is RDF and RDF schema, and its extension OWL.

xii
Semantic Web, ontologies, knowledge management and engineering, Web services, and Web processes. It serves
as the platform for exchange of both practical technologies and far reaching research.
Organization of the Book
This book is divided into 13 chapters and it is organized in a manner that allows a gradual progression of the
main subject toward more advanced topics. The first five chapters cover the logic and engineering approaches
needed to develop ontologies and bring into play semantics. Chapters VII and VIII introduce two technological
areas, Web services and Web processes, which have received a considerable amount of attention and focus from
the Semantic Web community. The remaining chapters, Chapters IX, X, XI, XII, and XIII, describe in detail
how semantics are being used to annotate Web services, discover Web services, and deploy semantic search
engines.
Chapter I introduces the concepts of syntactic and Semantic Web. The World Wide Web composed of HTML
documents can be characterized as a syntactic or visual Web since documents are meant only to be displayed
by Web browsers. In the visual Web, machines cannot understand the meaning of the information present in
HTML pages, since they are mainly made up of ASCII codes and images. The visual Web prevents computers
from automating information processing, integration, and interoperability. Currently the Web is undergoing an
evolution and different approaches are being sought for adding semantics to Web pages and resources in general.
Due to the widespread importance of integration and interoperability for intra- and inter-business processes, the
research community has already developed several semantic standards such as the resource description frame-
work (RDF), RDF schema (RDFS) and the Web Ontology Language (OWL). RDF, RDFS and OWL standards
enable the Web to be a global infrastructure for sharing both documents and data, which make searching and
reusing information easier and more reliable as well. RDF is a standard for creating descriptions of information,
especially information available on the World Wide Web. What XML is for syntax, RDF is for semantics. The
latter provides a clear set of rules for providing simple descriptive information. OWL provides a language for
defining structured Web-based ontologies which allows a richer integration and interoperability of data among
communities and domains. Even though the Semantic Web is still in its infancy, there are already applications
and tools that use this conceptual approach to build Semantic Web-based systems. Therefore, in this chapter, we
present the state of the art of the applications that use semantics and ontologies. We describe various applica-
tions ranging from the use of Semantic Web services, semantic integration of tourism information sources, and
semantic digital libraries to the development of bioinformatics ontologies.
Chapter II introduces a number of formal logical languages which form the backbone of the Semantic Web.
They are used for the representation of both ontologies and rules. The basis for all languages presented in this
chapter is the classical first-order logic. Description logics is a family of languages which represent subsets of
first-order logic. Expressive description logic languages form the basis for popular ontology languages on the
SemanticWeb. Logic programming is based on a subset of first-order logic, namely Horn logic, but uses a slightly
different semantics and can be extended with non-monotonic negation. Many Semantic Web reasoners are based
on logic programming principles and rule languages for the Semantic Web based on logic programming are
an ongoing discussion. Frame logic allows object-oriented style (frame-based) modeling in a logical language.
RuleML is an XML-based syntax consisting of different sub-languages for the exchange of specifications in
different logical languages over the Web.
In computer science, ontologies are defined as formal, explicit specifications of shared conceptualizations.
Their origin in this discipline can be referred back to 1991, in the context of the DARPAknowledge sharing effort.
Since then, considerable progress has been made and ontologies are now considered as a commodity that can be
used for the development of a large number of applications in different fields, such as knowledge management,
natural language processing, e-commerce, intelligent integration information, information retrieval, database

xiii
In Chapter VI we describe and explain how reasoning can be carried out in on the Semantic Web.
Reasoning is the process needed for using logic. Efficiently performing this process is a prerequisite
for using logic to present information in a declarative way and to construct models of reality. In this
chapter we describe both what the reasoning over the formal semantics of description logic amounts
to and to, and illustrate how formal reasoning can (and cannot!) be used for understanding real world
semantics given a good formal model of the situation. We first describe how the formal semantics of
description logic can be understood in terms of completing oriented labeled graphs. In other words we
interpret the formal semantics of description logic as rules for inferring implied arrows in a dots and
arrows diagram. We give an essentially complete “graphical” overview of OWL that may be used as
an introduction to the semantics of this language. We then touch on the algorithmic complexity of this
graph completion problem giving a simple version of the tableau algorithm, and give pointers to exist-
ing implementations of OWL reasoners. The second part deals with semantics as the relation between
a formal model and reality. We give an extended example building up a small toy ontology of concepts
useful for describing buildings, their physical layout and physical objects such as wireless routers and
printers in the turtle notation for OWL. We then describe a (imaginary) building with routers in these
terms. We explain how such a model can help in determining the location of resources given an ideal-
ized wireless device that is in or out of range of a router. We emphasize how different assumptions on
the way routers and buildings work are formalized and made explicit in the formal semantics of the
logical model. In particular we explain the sharp distinction between knowing some facts and knowing
all facts (open, versus closed world assumption). The example also illustrates the fact that reasoning is
no magical substitute for insufficient data. This section should be helpful when using ontologies and
incomplete real world knowledge in applications.
Chapter VII gives an introduction to Web service technology. Web services are emerging technolo-
gies that allow programmatic access to resources on the Internet. Web services provide a means to create
distributed systems which are loosely couple, meaning that the interaction between the client and service
is not dependent on one having any knowledge of the other. This type of interaction between components
is defined formally by the service-oriented architecture (SOA). The backbone of Web services is XML.
Extensible Markup Language (XML) is a platform independent data representation which allows the
flexibility that Web services need to fulfill their promise. Simple object access protocol, or SOAP, is
the XML-based protocol that governs the communication between a service and the client. It provides
a platform and programming language independent way for Web services to exchange messages. Web
Service Description Language (WSDL) is an XML-based language for describing a service. It describes
all the information needed to advertise and invoke a Web service. UDDI is a standard for storing WSDL
files as a registry so that they can be discovered by clients. There are other standards for describing
policy, security, reliability, and transactions of Web services that are described in the chapter. With all
this power and flexibility, Web services are fairly easy to build. Standard software engineering practices
are still valid with this new technology though tool support is making some of the steps trivial. Initially,
we design the service as a UML class diagram. This diagram can then be translated (either by hand or by
tools like Posiden) to a Java interface. This class can become a Web service by adding some annotations
to the Java code that will be used to create the WSDL file for the service. At this point, we need only to
implement the business logic of the service to have a system that is capable of performing the needed
tasks. Next, the service is deployed on an application server, tested for access and logic correctness, and
published to a registry so that it can be discovered by clients.
In Chapter VIII we introduce and provide an overview of the Business Process Execution Language
for Web services (known as BPEL4WS or BPEL for short), an emerging standard for specifying the
behavior of Web services at different levels of details using business process modeling constructs. BPEL

xiv
represents a convergence between Web services and business process technology. It defines a model and a gram-
mar for describing the behavior of a business process based on interactions between the process and its partners.
Being supported by vendors such as IBM and Microsoft, BPEL is positioned as the “process language of the
Internet.” The chapter firstly introduces BPEL by illustrating its key concepts and the usage of its constructs to
define service-oriented processes and to model business protocols between interacting Web services. A BPEL
process is composed of activities that can be combined through structured operators and related through control
links. In addition to the main process flow, BPEL provides event handling, fault handling and compensation
capabilities. In the long-running business processes, BPEL applies correlation mechanism to route messages
to the correct process instance. On the other hand, BPEL is layered on top of several XML specifications such
as WSDL, XML schema and XPath. WSDL message types and XML schema type definitions provide the data
model used in BPEL processes, and XPath provides support for data manipulation. All external resources and
partners are represented as WSDL services. Next, to further illustrate the BPEL constructs introduced above, a
comprehensive working example of a BPEL process is given, which covers the process definition, XML schema
definition, WSDL document definition, and the process execution over a popular BPEL-compliant engine. Since
the BPEL specification defines only the kernel of BPEL, extensions are allowed to be made in separate docu-
mentations. The chapter reviews some perceived limitations of BPEL and extensions that have been proposed
by industry vendors to address these limitations. Finally, for an advanced discussion, the chapter considers the
possibility of applying formal methods and Semantic Web technology to support the rigorous development of
service-oriented processes using BPEL.
Web services show promise to address the needs of application integration by providing a standards-based
framework for exchanging information dynamically between applications. Industry efforts to standardize Web
service description, discovery and invocation have led to standards such as WSDL, UDDI, and SOAP respec-
tively. These industry standards, in their current form, are designed to represent information about the interfaces
of services, how they are deployed, and how to invoke them, but are limited in their ability to express the ca-
pabilities and requirements of services. This lack of semantic representation capabilities leaves the promise of
automatic integration of applications written to Web services standards unfulfilled. To address this, the Semantic
Web community has introduced Semantic Web services. Semantic Web services are the main topic of Chapter
IX. By encoding the requirements and capabilities of Web services in an unambiguous and machine-interpretable
form semantics make the automatic discovery, composition and integration of software components possible.
This chapter introduces Semantic Web services as a means to achieve this vision. It presents an overview of
Semantic Web services, their representation mechanisms, related work and use cases. Specifically, the chapter
contrasts various Semantic Web service representation mechanisms such as OWL-S, WSMO and WSDL-S and
presents an overview of the research work in the area of Web service discovery, and composition that use these
representation mechanisms.
Web services are software components that are accessible as Web resources in order to be reused by other
Web services or software. Hence, they function as middleware connecting different parties such as companies
or organizations distributed over the Web. In Chapter X, we consider the process of provisioning data about a
Web service to constitute a specification of the Web service. At this point, the question arises how a machine
may attribute machine-understandable meaning to this metadata. Therefore, we argue for the use of ontologies
for giving a formal semantics to Web service annotations, that is, we argue in favor of Semantic Web service
annotations. A Web service ontology defines general concepts such as service or operation as well as relations
that exist between such concepts. The metadata describing a Web service can instantiate concepts of the ontol-
ogy. This connection supports Web service developers to understand and compare the metadata of different
services described by the same or a similar ontology. Consequently, ontology-based Web service annotation
leverages the use, reuse and verification of Web services. The process of Semantic Web service annotation in
general requires input from multiple sources, that is legacy descriptions, as well as a labor-intensive modeling

xv
effort. Information about a Web service can be gathered for example from the source code of a service
(if annotation is done by a service provider), from the API documentation and description, from the
overall textual documentation of a Web service or from descriptions in WS* standards. Depending on
the structuredness of these sources, semantic annotations may (have to) be provided manually (e.g., if
full text is the input), semi-automatically (e.g. for some WS* descriptions) or fully automatically (e.g.,
if Java interfaces constitute the input). Hence, a semantic description of the signature of a Web service
may be provided by automatic means, while the functionality of Web service operations or pre- and
post-conditions of a Web service operation may only be modeled manually. Benefits of semantic speci-
fications of Web services include a common framework that integrates semantic descriptions of many
relevant Web service properties. It is the purpose of this chapter to explain the conceptual gap between
legacy descriptions and semantic specifications and to indicate how this gap is to be bridged.
Chapter XI deals with methods, algorithms and tools for Semantic Web service discovery. Semantic
Web has revolutionized, among other things, the implementation of Web services lifecycle. The core
phases of this lifecycle, such as service discovery and composition can be performed more effectively
through the exploitation of the semantics that annotate the service descriptions. This chapter focuses on
the phase of discovery due to its central role in every, service-oriented architecture. Hence, it surveys
existing approaches to Semantic Web service (SWS) discovery. Such discovery process is expected to
substitute existing keyword-based solutions (e.g., UDDI) in the near future, in order to overcome their
limitations. First, the architectural components of a SWS discovery ecosystem, along with potential
deployment scenarios, are discussed. Subsequently, a wide range of algorithms and tools that have been
proposed for the realization of SWS discovery are presented. The presentation of the various approaches
aims at outlining the key characteristics of each proposed solution, without delving into technology-
dependent details (e.g., service description languages). The descriptions of the tools included in this
chapter provide a starting point for further experimentation by the reader. In this respect, a brief tutorial
for a certain tool is provided as an appendix. Finally, key challenges and open issues, not addressed
by current systems, are identified (e.g., evaluation of service retrieval, mediation and interoperability
issues). The ultimate purpose of this chapter is to update the reader on the recent developments in this
area of the distributed systems domain and provide the required background knowledge and stimuli for
further research and experimentation in semantics-based service discovery.
Taking an abstract perspective,Web services can be considered as complex resources on theWeb, that
is, resources that might have more complex structure and properties than conventional data that is shared
on the Web. Recently, the Web service modeling ontology (WSMO) has been developed to provide a
conceptual framework for semantically describing Web services and their specific properties in detail.
WSMOrepresentsapromisingandrathergeneralframeworkforSemanticWebservicedescriptionandis
currently applied in various European projects in the area of Semantic Web services and Grid computing.
In Chapter XII, we discuss how Web service discovery can be achieved within the WSMO Framework.
First, we motivate Semantic Web services and the idea of applying semantics to Web services. We give
a brief high-level overview of the Web service modeling ontology and present the main underlying
principles. We discuss the distinction between two notions that are often intermixed when talking about
Semantic Web services and thus provide a proper conceptual grounding for our framework, namely we
strictly distinguish between services and Web services. Consequently, we distinguish between service
discovery and web service discovery, whereas only the latter is then considered in detail in the chapter.
Since in open environments like the Web, the assumption of homogeneous vocabularies and descriptions
breaks, we briefly consider mediation and discuss its role in service and Web service Discovery. Hereby,
wetrytoidentifyrequirementsonthediscoveryprocessandrespectivesemanticdescriptionswhichallow
facing heterogeneity and scalability at the same time. We then present a layered model of successively

xvi
more detailed and precise perspectives on Web services and consider Web service descriptions on each
of them. For the two most fine-grained levels, we then discuss how to detect semantic matches between
requested and provided functionalities. Based on our model, we are able to integrate and extend matching
notions that have been known in the area already. First, we consider Web services essentially as concepts
in an ontology, where required inputs and the condition under which a requested service actually can be
delivered is neglected. Then, we move forward to a more detailed level of description, where inputs and
respective preconditions for service delivery are no longer ignored. We show how to adapt and extend
the simpler model and matching notions from before to adequately address richer semantic descriptions
on this level. The various levels of descriptions are meant to support a wide range of scenarios that can
appear in practical applications, requiring different levels of details in the description of Web services
and client requests, as well as different precision and performance.
Chapter XIII focuses on semantic search engines and data integration systems. As the use of the
World Wide Web has become increasingly widespread, the business of commercial search engines
has become a vital and lucrative part of the Web. Search engines are common place tools for virtually
every user of the Internet; and companies, such as Google and Yahoo!, have become household names.
Semantic search engines try to augment and improve traditional Web search engines by using not just
words, but concepts and logical relationships. We believe that data integration systems, domain ontolo-
gies and schema based peer-to-peer architectures are good ingredients for developing semantic search
engines with good performance. Data integration is the problem of combining data residing at different
autonomous sources, and providing the user with a unified view of these data; the problem of designing
data integration systems is important in current real world applications, and is characterized by a number
of issues that are interesting from a theoretical point of view. Schema-based peer-to-peer networks are
a new class of peer-to-peer networks, combining approaches from peer-to-peer as well as from the data
integration and Semantic Web research areas. Such networks build upon peers that use metadata (ontolo-
gies) to describe their contents and semantic mappings among concepts of different peers’ ontologies.
In this chapter, we will provide empirical evidence for our hypothesis. More precisely, we will describe
two projects, SEWASIE and WISDOM, which rely on these architectural features and developed key
semantic search functionalities; they both exploit the MOMIS (www.dbgroup.unimo.it/Momis/) data
integration system. The first, SEWASIE (www.sewasie.org), rely on a two-level ontology architecture:
the low level, called the peer level contains a data integration system; the second one, called super-peer
level integrates peers with semantically related content (i.e., related to the same domain). The second,
WISDOM (www.dbgroup.unimo.it/wisdom/), is based on an overlay network of semantic peers: each
peer contains a data integration system. The cardinal idea of the project is to develop a framework that
supports a flexible yet efficient integration of the semantic content.

xvii
This book describes the most recent advances in Semantic Web and results of a collaborative effort
towards the development of a comprehensive manuscript that exposes the major issues related to this
new area of research. I wish to express my gratitude to everyone who contributed to making this book a
reality. This project is the accumulation of months of work by many dedicated researchers. Some of the
most well-know researcher in the world have dedicated their precious time to share their experience and
knowledge with you. It would not have been possible for me to produce this work without their help.
Acknowledgments

Chapter I
The Syntactic and the Semantic Web
Jorge Cardoso
University of Madeira, Portugal
Copyright © 2007, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Motivation for thE sEMantic
WEb
The World Wide Web (WWW) was developed
in 1989 at the European Laboratory for Particle
Physics (CERN) in Geneva, Switzerland. It was
Tim Berners-Lee who developed the first proto-
type of the World Wide Web intended to serve as
an information system for physicists.
Bytheendof1990,TimBerners-Leehadwrit-
tenthefirstbrowsertoretrieveandviewhypertext
documents and wrote the first Web server—the
software, which stores Web pages on a computer
for others to access. The system was originally
developed to allow information sharing within
internationally dispersed working groups. The
originalWWWconsistedofdocuments(i.e.,Web
pages) and links between documents.
Browsers and Web server users grew. They
became more and more attractive as an infor-
mation sharing infrastructure. The Web became
even more interesting as the amount of available
abstract
This chapter gives an overview of the evolution of the Web. Initially, Web pages were intended only for
human consumption and were usually displayed on a Web browser. New Internet business models, such
as B2B and B2C, required organizations to search for solutions to enable a deep interoperability and
integration between their systems and applications. One emergent solution was to define the informa-
tion on the Web using semantics and ontologies in a way that it could be used by computers not only
for display purposes, but also for interoperability and integration. The research community developed
standards to semantically describe Web information such as the resource description framework and
the Web Ontology Language. Ontologies can assist in communication between human beings, achieve
interoperability among software systems, and improve the design and the quality of software systems.
These evolving Semantic Web technologies are already being used to build semantic Web based systems
such as semantic Web services, semantic integration of tourism information sources, and semantic digital
libraries to the development of bioinformatics ontologies.

information of every sort increased. A Web page
can be accessed by a URL (uniform resource
locator) through the hypertext transfer protocol
(HTTP) using a Web browser (e.g., Internet Ex-
plorer, Netscape, Mozilla, Safari).
Currently, the World Wide Web is primar-
ily composed of documents written in HTML
(Hyper Text Markup Language), a language that
is useful for visual presentation. HTML is a set
of “markup” symbols contained in a Web page
intended for display on a Web browser. Most
of the information on the Web is designed only
for human consumption. Humans can read Web
pages and understand them, but their inherent
meaning is not shown in a way that allows their
interpretation by computers.
The information on the Web can be defined in
a way that can be used by computers not only for
displaypurposes,butalsoforinteroperabilityand
integrationbetweensystemsandapplications.One
waytoenablemachine-to-machineexchangeand
automated processing is to provide the informa-
tion in such a way that computers can understand
it. This is precisely the objective of the semantic
Web—to make possible the processing of Web
information by computers.
The Semantic Web is not a separate Web but an
extensionofthecurrentone,inwhichinformation
is given well-defined meaning, better enabling
computers and people to work in cooperation.
(Berners-Lee, Hendler, et al., 2001)
The next generation of the Web will combine
existing Web technologies with knowledge rep-
resentation formalisms (Grau, 2004).
The Semantic Web was made through incre-
mental changes, by bringing machine-readable
descriptions to the data and documents already
on the Web. Figure 1 illustrates the various de-
veloped technologies that made the concept of
the Semantic Web possible. As already stated,
the Web was originally a vast set of static Web
pages linked together. Many organizations still
usestaticHTMLfilestodelivertheirinformation
on the Web. However, to answer to the inherent
dynamic nature of businesses, organizations are
using dynamic publishing methods which offer
great advantages over Web sites constructed
from static HTML pages. Instead of a Web site
comprising a collection of manually constructed
HTML pages, server-side applications and data-
base access techniques are used to dynamically
Figure 1. Evolution of the Web

create Web pages directly in response to requests
from user browsers. This technique offers the
opportunity to deliver Web content that is highly
customized to the needs of individual users.
Nevertheless, the technologies available to
dynamically create Web pages based on database
information were insufficient for the require-
ments of organizations looking for application
integration solutions. Businesses required their
heterogeneous systems and applications to com-
municate in a transactional manner. The Exten-
sible Markup Language (XML, 2005) was one of
most successful solutions developed to provide
business-to-businessintegration.XMLbecamea
means of transmitting unstructured, semi-struc-
tured,andevenstructureddatabetweensystems,
enhancing the integration of applications and
businesses.
Unfortunately, XML-based solutions for
applications and systems integration were not
sufficient, since the data exchanged lacked an
explicitdescriptionofitsmeaning.Theintegration
of applications must also include a semantic inte-
gration.Semanticintegrationandinteroperability
is concerned with the use of explicit semantic
descriptions to facilitate integration.
CurrentlytheWebisundergoingevolution(as
illustrated in Figure 2) and different approaches
arebeingsoughtforsolutionstoaddingsemantics
to Web resources. On the left side of Figure 2, a
graphrepresentationofthesyntacticWebisgiven.
Resources are linked together forming the Web.
There is no distinction between resources or the
links that connect resources. To give meaning to
resourcesandlinks,newstandardsandlanguages
are being investigated and developed. The rules
and descriptive information made available by
these languages allow the type of resources on
the Web and the relationships between resources
to be characterized individually and precisely, as
illustrated on the right side of Figure 2.
Due to the widespread importance of inte-
gration and interoperability for intra- and inter-
business processes, the research community has
tackled this problem and developed semantic
standardssuchastheresourcedescriptionframe-
work (RDF) (RDF, 2002) and the Web Ontology
Language (OWL) (OWL, 2004). RDF and OWL
standardsenabletheWebtobeaglobalinfrastruc-
ture for sharing both documents and data, which
make searching and reusing information easier
and more reliable as well. RDF is a standard for
creating descriptions of information, especially
information available on the World Wide Web.
What XML is for syntax, RDF is for semantics.
The latter provides a clear set of rules for provid-
ingsimpledescriptiveinformation.OWLprovides
a language for defining structured Web-based
ontologies which allows a richer integration and
interoperability of data among communities and
domains.
Figure 2. Evolution of the Web

thE visual and syntactic WEb
The World Wide Web composed of HTML docu-
ments can be characterized as a visual Web since
documentsaremeantonlytobedisplayedbyWeb
browsers. In the visual Web, machines cannot
understand the meaning of the information pres-
ent in HTML pages, since they are mainly made
up of ASCII codes and images. The visual Web
preventscomputersfromautomatinginformation
processing, integration, and interoperability.
WithHTMLdocuments,presentationalmeta-
data is used to assign information to the content
andaffectitspresentation.Metadataisdataabout
dataandcanbeusedtodescribeinformationabout
aresource.Aresourcecan,forexample,beaWeb
page, a document, an image, or a file. Examples
of metadata that can be associated with a file in-
clude its title, subject, author, and size. Metadata
mostly consists of a set of attribute value pairs
that gives information about characteristics of a
document. For example,
title = Semantic Web: Technologies and Applications
subject = Semantic Web
author = Jorge Cardoso
size = 6 Kbytes
In HTML pages, the content is marked-up
with metadata. Specific tags are used to indicate
the beginning and end of each element. For ex-
ample, to specify that the title of the Web page is
“SemanticWeb:TechnologiesandApplications,”
the text is marked-up using the tag Title. To
inform the Web browser that “Motivation for the
Semantic Web” is a heading, the text is marked-
up as a heading element, using level-one h1
heading tag such as:
Title Semantic Web: Technologies and Applications
/Title
h Motivation for the Semantic Web /h
One restriction of HTML is that it is semanti-
cally limited. There is a lack of rich vocabulary
of element types capable of capturing the mean-
ing behind every piece of text. For example,
Google search engine reads a significant number
of the world’s Web pages and allows users to
type in keywords to find pages containing those
keywords. There is no meaning associated to the
keywords.Googleonlycarriesoutsimplematches
between the keywords and the words in its data-
base. The metadata of HTML is not considered
when searching for a particular set of keywords.
Even if Google would use HTML metadata to
answer queries, the lack of semantics of HTML
tags would most likely not improve the search.
On the other hand, the Syntactic Web is the
collection of documents in the World Wide Web
that contain data not just meant to be rendered
by Web browsers, but also to be used for data
integration and interoperability purposes. To
be able to “understand” data, a computer needs
metadata which will be provided by some kind of
markuplanguage.Awidespreadmarkuplanguage
is XML. With HTML the set of tags available
to users is predefined and new tags cannot be
added to the language. In contrast, XML is an
extremely versatile markup language allowing
users to be capable of creating new tags to add
syntactic meaning to information.
In order to allow data integration, the mean-
ing of XML document content is determined by
agreements reached between the businesses that
will be exchanging data. Agreements are usually
defined using a standardized document, such
as the document type definition (DTD) (XML,
2005) or the XML schema definition (XSD)
(XMLschema, 2005) that specifies the structure
and data elements of the messages exchanged.
These agreements can then be used by applica-
tions to act on the data.
In a typical organization, business data is
stored in many formats and across many systems
and databases throughout the organization and
with partner organizations. To partially solve
integration problems, organizations have been

using solutions such as XML to exchange or
movebusinessdatabetweeninformationsystems.
Prior to XML, an organization had to hardcode
modules to retrieve data from data sources and
construct a message to send to other applica-
tions. The adoption of XML accelerates the
constructionofsystemsthatintegratedistributed,
heterogeneous data. The XML language allows
the flexible coding and display of data, by using
metadata to describe the structure of data (e.g.,
DTD or XSD).
The first step necessary to accomplish data
integration using XML technologies consists of
taking the raw data sources (text, spreadsheets,
relational tables, etc) and converting them into
well-formed XML documents. The next step is to
analyze and document its structure by creating a
DTD or XSD for each of the data sources.
One limitation of XML is that it can only
define the syntax of documents. XML data does
not include information which can be used to de-
scribethemeaningofthetagsused.Thefollowing
example illustrates an XML instance.
student
name John Hall /name
id 669-- /id
major Philosophy /major
/student
In this example, the XML instance indicates
there is a student named “John Hall.” His id
is “669-33-2555,” but no information is provided
about the meaning of an id or the meaning of
the different fields that compose an id. Finally,
the student’s major is “Philosophy.” No infor-
mation is provided concerning the relationship of
this major with the other majors that are given
at the University John attends.
unstructurEd,
sEMistructurEd,
and structurEd data
Data breaks down into three broad categories
(Figure 3): unstructured, semistructured, and
structured. Highly unstructured data comprises
free-form documents or objects of arbitrary sizes
andtypes.Attheotherendofthespectrum,struc-
tured data is what is typically found in databases.
Every element of data has an assigned format and
significance.
unstructured data
Unstructured data is what we find in text, files,
video,e-mails,reports,PowerPointpresentations,
Ph.D.

David

B.Sc.
9
Michael

M.Sc.
6
Rick

Ph.D.

Robert

B.Sc.

John

degree
age
name
id
Ph.D.

David

B.Sc.
9
Michael

M.Sc.
6
Rick

Ph.D.

Robert

B.Sc.

John

degree
age
name
id
University
Student ID=
NameJohn/Name
Age/Age
DegreeB.Sc./Degree
/Student
Student ID=
NameDavid/Name
Age/Age
DegreePh.D. /Degree
/Student
.
/University
University
Student ID=
NameJohn/Name
Age/Age
DegreeB.Sc./Degree
/Student
Student ID=
NameDavid/Name
Age/Age
DegreePh.D. /Degree
/Student
.
/University
The university has 600
students.
John s ID is number , he is
years old and already
holds a B.Sc. degree.
David s ID is number , he is
years old and holds a
Ph.D. degree. Robert s ID is
number , he is years old
and also holds the same
degree as David, a Ph.D.
degree.
The university has 600
students.
John s ID is number , he is
years old and already
holds a B.Sc. degree.
David s ID is number , he is
years old and holds a
Ph.D. degree. Robert s ID is
number , he is years old
and also holds the same
degree as David, a Ph.D.
degree.
unstructured data semi-structured data structured data
Figure 3. Unstructured, semistructured, and structured data

6
voice mail, office memos, and images. Data can
be of any type and does not necessarily follow
any format, rules, or sequence. For example, the
datapresentonHTMLWebpagesisunstructured
and irregular.
Unstructured data does not readily fit into
structureddatabasesexceptasbinarylargeobjects
(BLOBs-binarylargeobjects).Althoughunstruc-
tureddatacanhavesomestructure—forexample,
e-mails have addressees, subjects, bodies, and so
forth, and HTML Web pages have a set of pre-
definedtags—theinformationisnotstoredinsuch
a way that it will allow for easy classification, as
the data are entered in electronic form.
semistructured data
Semistructured data lie somewhere in between
unstructuredandstructureddata.Semistructured
data are data that have some structure, but are
not rigidly structured. This type of data includes
unstructured components arranged according to
some predetermined structure. Semistructured
data can be specified in such a way that it can be
queried using general-purpose mechanisms.
Semistructured data are organized into enti-
ties. Similar entities are grouped together, but
entities in the same group may not have the same
attributes.Theorderofattributesisnotnecessarily
important and not all attributes may be required.
The size and type of same attributes in a group
may differ.
An example of semistructured data is a Cur-
riculum Vitae. One person may have a section of
previousemployments,anotherpersonmayhave
a section on research experience, and another
may have a section on teaching experience. We
can also find a CV that contains two or more of
these sections.
A very good example of a semistructured
formalism is XML which is a de facto standard
for describing documents that is becoming the
universal data exchange model on the Web and
is being used for business-to-business transac-
tions. XML supports the development of semis-
tructured documents that contain both metadata
and formatted text. Metadata is specified using
XMLtagsanddefinesthestructureofdocuments.
Withoutmetadata,applicationswouldnotbeable
to understand and parse the content of XML
documents. Compared to HTML, XML provides
explicitdatastructuring.XMLusesDTDorXSD
as schema definitions for the semistructured data
present in XML documents. Figure 3 shows the
(semi)structureofanXMLdocumentcontaining
students’ records at a university.
structured data
In contrast, structured data are very rigid and
describe objects using strongly typed attributes,
which are organized as records or tuples. All re-
cords have the same fields. Data are organized in
entities and similar entities are grouped together
using relations or classes. Entities in the same
group have the same attributes. The descriptions
for all the entities in a schema have the same
defined format, predefined length, and follow
the same order.
Structured data have been very popular since
the early days of computing and many organiza-
tionsrelyonrelationaldatabasestomaintainvery
largestructuredrepositories.Recentsystems,such
as CRM (customer relationship management),
ERP (enterprise resource planning), and CMS
(content management systems) use structured
data for their underlying data model.
lEvEls of sEMantics
As we have seen previously, semantics is the
study of the meaning of signs, such as terms or
words. Depending on the approaches, models, or
methods used to add semantics to terms, differ-
ent degrees of semantics can be achieved. In this
section we identify and describe four represen-

tations that can be used to model and organize
concepts to semantically describe terms, that is,
controlled vocabularies, taxonomies, thesaurus,
andontologies.Thesefourmodelrepresentations
are illustrated in Figure 4.
controlled vocabularies
Controlled vocabularies are at the weaker end of
thesemanticspectrum.Acontrolledvocabularyis
a list of terms (e.g., words, phrases, or notations)
that have been enumerated explicitly. All terms
in a controlled vocabulary should have an unam-
biguous, non-redundant definition. A controlled
vocabularyisthesimplestofallmetadatamethods
and has been extensively used for classification.
For example, Amazon.com has the following
(Table 1) controlled vocabulary which can be
selected by the user to search for products.
Controlled vocabularies limit choices to an
agreed upon unambiguous set of terms. In cata-
loguing applications, users can be presented with
list of terms from which they can pick the term
to describe an item for cataloguing. The main
objectiveofacontrollingvocabularyistoprevent
usersfromdefiningtheirowntermswhichcanbe
ambiguous, meaningless, or misspelled.
controlled vocabulary
taxonomy
thesaurus
ontology
strong semantics
Weak semantics
Structure, hierarchy,
parent-child relationships
Equivalence, homographic, hierarchical,
and associative relationships
Relationships,
constraints, rules
+
+
+
Figure 4. Levels of semantics
Books Electronics Travel
Popular Music Camera Photo Cell Phones Service
Music Downloads Software Outlet
Classical Music Tools Hardware Auctions
DVD Office Products zShops
VHS Magazines Everything Else
Apparel Sports Outdoors Scientific Supplies
Yellow Pages Outdoor Living Medical Supplies
Restaurants Kitchen Indust. Supplies
Movie Showtimes Jewelry Watches Automotive
Toys Beauty Home Furnishings
Baby Gourmet Food Beta Lifestyle
Computers Musical Instruments Pet Toys
Video Games Health/Personal Care Arts Hobbies
Table 1. Controlled vocabulary used by Amazon.com

taxonomies
A taxonomy is a subject-based classification that
arrangesthetermsinacontrolledvocabularyinto
a hierarchy without doing anything further. The
first users of taxonomies were biologists in the
classification of organisms. They have employed
thismethodtoclassifyplantsandanimalsaccord-
ing to a set of natural relationships. A taxonomy
classifies terms in the shape of a hierarchy or
tree. It describes a word by making explicit its
relationship with other words. Figure 5 shows a
taxonomy of merchandise that can be bought for
a home.
The hierarchy of a taxonomy contains parent-
child relationships, such as “is subclass of” or “is
superclassof.”Auserorcomputercancomprehend
the semantics of a word by analyzing the exist-
ing relationship between the word and the words
around it in the hierarchy.
thesaurus
Athesaurusisanetworkedcollectionofcontrolled
vocabulary terms with conceptual relationships
between terms. A thesaurus is an extension of a
taxonomy by allowing terms to be arranged in
a hierarchy and also allowing other statements
and relationships to be made about the terms. A
thesauruscaneasilybeconvertedintoataxonomy
or controlled vocabulary. Of course, in such con-
version, expressiveness and semantics are lost.
Table 2 shows an example1
of a thesaurus listing
for the term academic achievement.
According to the National Information Stan-
dards Organization (NISO, 2005), there are four
different types of relationships that are used in a
thesaurus: equivalence, homographic, hierarchi-
cal, and associative.
• Equivalence: An equivalence relation says
thatatermt1
hasthesameornearlythesame
meaning as a term t2
.
• Homographic: Two terms, t1
and t2
, are
called homographic if term t1
is spelled the
same way as a term t2
, but has a different
meaning.
• Hierarchical: This relationship is based on
the degrees or levels of “is subclass of” and
“is superclass of” relationships. The former
represents a class or a whole, and the latter
refers to its members or parts.
• Associative: This relationship is used to
link terms that are closely related in mean-
ing semantically but not hierarchically. An
example of an associative relationship can
be as simple as “is related to” as in term t1
“is related to” term t2
.
ontologies
Ontologies are similar to taxonomies but use
richer semantic relationships among terms and
attributes, as well as strict rules about how to
specify terms and relationships. In computer
science, ontologies have emerged from the area
of artificial intelligence. Ontologies have gener-
ally been associated with logical inferencing and
recently have begun to be applied to the semantic
Web.
Furnishings
Printer
Scanner
Modem
Network
Computers
Hardware
Software
Kitchen
Living room
Bathroom
Stove
Cupboard
Dinning table
Silverware
Tableware
Coffee table
Futon
Sofa
Lavatory
Toilet
Bathtub
Antivirus
OS
Editing
Spreadsheet
Drawing
Home
Figure 5. Example of a taxonomy

9
An ontology is a shared conceptualization of
the world. Ontologies consist of definitional as-
pects such as high-level schemas and assertional
aspects such as entities, attributes, interrelation-
ships between entities, domain vocabulary and
factual knowledge—all connected in a semantic
manner (Sheth, 2003). Ontologies provide a com-
mon understanding of a particular domain. They
allow the domain to be communicated between
people, organizations, and application systems.
Ontologies provide the specific tools to organize
andprovideausefuldescriptionofheterogeneous
content.
In addition to the hierarchical relationship
structureoftypicaltaxonomies,ontologiesenable
cross-nodehorizontalrelationshipsbetweenenti-
ties, thus enabling easy modeling of real-world
information requirements. Jasper and Uschold
(1999) identify three major uses of ontologies:
1. Toassistincommunicationbetweenhuman
beings
2. Toachieveinteroperabilityamongsoftware
systems
3. To improve the design and the quality of
software systems
An ontology is technically a model which
looks very much like an ordinary object model
in object-oriented programming. It consists of
classes,inheritance,andproperties(Fensel,2001).
In many situations, ontologies are thought of as
knowledge representation.
sEMantic WEb architEcturE
The Semantic Web identifies a set of technolo-
gies, tools, and standards which form the basic
buildingblocksofaninfrastructuretosupportthe
vision of the Web associated with meaning. The
semanticWebarchitectureiscomposedofaseries
ofstandardsorganizedintoacertainstructurethat
is an expression of their interrelationships. This
Relationship Term
Used for
Grade point Average
Scholastic Achievement
School Achievement
Narrower than
Academic Overachievement
Academic Underachievement
College Academic Achievement
Mathematics Achievement
Reading Achievement
Science Achievement
Broader than Achievement
Related to
Academic Achievement Motivation
Academic Achievement Prediction
Academic Aptitude
Academic Failure
Academic Self Concept
Education
Educational Attainment Level
School Graduation
School Learning
School Transition
Table 2. Example of a thesaurus listing for the term academic achievement

0
architecture is often represented using a diagram
firstproposedbyTimBerners-Lee(Berners-Lee,
Hendler et al., 2001). Figure 6 illustrates the dif-
ferent parts of the semantic Web architecture. It
starts with the foundation of URIs and Unicode.
On top of that we can find the syntactic interoper-
ability layer in the form of XML, which in turn
underlies RDF and RDF schema (RDFS). Web
ontology languages are built on top of RDF(S).
The three last layers are the logic, proof, and
trust, which have not been significantly explored.
Some of the layers rely on the digital signature
component to ensure security.
In the following sections we will briefly de-
scribe these layers. While the notions presented
have been simplified, they provide a reasonable
conceptualization of the various components of
the semantic Web.
uri and unicode
A universal resource identifier (URI) is a format-
ted string that serves as a means of identifying
abstractorphysicalresource.AURIcanbefurther
classified as a locator, a name, or both. Uniform
resource locator (URL) refers to the subset of
URI that identifies resources via a representation
of their primary access mechanism. An uniform
resource name (URN) refers to the subset of URI
that is required to remain globally unique and
persistent even when the resource ceases to exist
or becomes unavailable. For example:
• TheURLhttp://dme.uma.pt/jcardoso/index.
htmidentifiesthelocationfromwhereaWeb
page can be retrieved
• TheURNurn:isbn:3-540-24328-3identifies
a book using its ISBN
Unicode provides a unique number for every
character, independently of the underlying plat-
form, program, or language. Before the creation
ofunicode,therewerevariousdifferentencoding
systems. The diverse encoding made the ma-
nipulation of data complex. Any given computer
needed to support many different encodings.
There was always the risk of encoding conflict,
since two encodings could use the same number
for two different characters, or use different
numbers for the same character. Examples of
older and well known encoding systems include
ASCII and EBCDIC.
Figure 6. Semantic Web layered architecture (Berners-Lee, Hendler, et al., 2001)

XMl
XML is accepted as a standard for data inter-
change on the Web allowing the structuring of
data on the Web but without communicating the
meaning of the data. It is a language for semis-
tructureddataandhasbeenproposedasasolution
for data integration problems, because it allows
a flexible coding and display of data, by using
metadata to describe the structure of data (using
DTD or XSD).
In contrast to HTML, with XML it is possible
to create new markup tags, such as first_name,
which carry some semantics. However, from
a computational perspective, a tag like first_
name is very similar to the HTML tag h1.
While XML is highly helpful for a syntactic
interoperability and integration, it carries as
much semantics as HTML. Nevertheless, XML
solved many problems which have earlier been
impossible to solve using HTML, that is, data
exchange and integration.
A well-formed XML document creates a bal-
anced tree of nested sets of open and closed tags,
each of which can include several attribute-value
pairs. The following structure shows an example
of an XML document identifying a “Contact” re-
source. The document includes various metadata
markuptags,suchasfirst_name,last_name,
andemail,whichprovidevarious details about
a contact.
Contact contact_id=“”
first_name Jorge /first_name
last_name Cardoso /last_name
organization University of Madeira /organiza-
tion
email jcardoso@uma.pt /email
phone + 9 0 6 /phone
/Contact
While XML has gained much of the world’s
attention it is important to recognize that XML is
simply a way of standardizing data formats. But
fromthepointofviewofsemanticinteroperability,
XMLhaslimitations.Onesignificantaspectisthat
there is no way to recognize the semantics of a
particulardomainbecauseXMLaimsatdocument
structure and imposes no common interpretation
of the data (Decker, Melnik et al., 2000). Another
problem is that XML has a weak data model in-
capable of capturing semantics, relationships, or
constraints.WhileitispossibletoextendXMLto
incorporaterichmetadata,XMLdoesnotallowfor
supporting automated interoperability of system
without human involvement. Even though XML
is simply a data-format standard, it is part of the
setoftechnologiesthatconstitutethefoundations
of the semantic Web.
rdf
At the top of XML, the World Wide Web Consor-
tium(W3C)hasdevelopedtheResourceDescrip-
tion Framework (RDF) (RDF, 2002) language to
standardize the definition and use of metadata.
Therefore, XML and RDF each have their merits
as a foundation for the semantic Web, but RDF
provides more suitable mechanisms for develop-
ing ontology representation languages like OIL
(Connolly, van Harmelen, et al., 2001).
RDF uses XML and it is at the base of the
semantic Web, so that all the other languages
corresponding to the upper layers are built on
top of it. RDF is a formal data model for machine
understandablemetadatausedtoprovidestandard
descriptions of Web resources. By providing a
standard way of referring to metadata elements,
specific metadata element names, and actual
metadatacontent,RDFbuildsstandardsforXML
applications so that they can interoperate and
intercommunicate more easily, facilitating data
and system integration and interoperability. At
first glance it may seem that RDF is very similar
to XML, but a closer analysis reveals that they
are conceptually different. If we model the in-
formation present in a RDF model using XML,
human readers would probably be able to infer
the underlying semantic structure, but general
purpose applications would not.

RDF is a simple general-purpose metadata
languageforrepresentinginformationintheWeb
and provides a model for describing and creat-
ing relationships between resources. A resource
can be a thing such as a person, a song, or a Web
page. With RDF it is possible to add predefined
modeling primitives for expressing semantics of
data to a document without making any assump-
tions about the structure of the document. RDF
defines a resource as any object that is uniquely
identifiable by a Uniform Resource Identifier
(URI). Resources have properties associated to
them.Propertiesareidentifiedbyproperty-types,
and property-types have corresponding values.
Property-typesexpresstherelationshipsofvalues
associated with resources. The basic structure of
RDFisverysimpleandbasicallyusesRDFtriples
in the form of subject, predicate, object.
• Subject: A thing identified by its URL
• Predicate:Thetypeofmetadata,alsoidenti-
fied by a URL (also called the property)
• Object: The value of this type of meta-
data
RDF has a very limited set of syntactic con-
structs, no other constructs except for triples is
allowed. Every RDF document is equivalent to
an unordered set of triples. The example from
Figure 7 describes the following statement using
a RDF triple:
Jorge Cardoso created the Jorge Cardoso Home
Page.
The Jorge Cardoso Home Page is a resource.
This resource has a URI of http://dme.uma.pt/
jcardoso/, and it has a property, “creator,” with
the value “Jorge Cardoso.”
The graphic representation of Figure 7 is ex-
pressed in RDF with the following statements:
? xml version=”.0” ?
RDF xmlns = “http://w.org/TR/999/PR-rdf-syntax-
99900#”
xmlns:DC = “http://dublincore.org/00/0//
dces#”
Description about = “http://dme.uma.pt/jcar-
doso/”
DC:Creator Jorge Cardoso /DC:Creator
/Description
/RDF
Thefirstlinesofthisexampleusenamespaces
toexplicitlydefinethemeaningofthenotionsthat
are used. The first namespace xmlns:rdf=”http://
w3.org/TR/1999/PR-rdf-syntax-19990105#”
refers to the document describing the syntax of
RDF. The second namespace http://dublincore.
org/2003/03/24/dces# refers to the description
of the Dublin Core (DC), a basic ontology about
authors and publications.
The Dublin Core (DC, 2005) is a fifteen ele-
ment metadata set that was originally developed
http://dme.uma.pt/jcardoso/ Jorge Cardoso
Creator
resource Property type Property value
(subject, predicate, object)
Figure 7. Graphic representation of a RDF statement

toimproveresourcediscoveryontheWeb.Tothis
end, the DC elements were primarily intended to
describe Web-based documents. Examples of the
Dublin Core metadata include:
• Title: The title of the resource
• Subject: Simple keywords or terms taken
from a list of subject headings
• Description: A description or abstract
• Creator:Thepersonororganizationprimar-
ily responsible for the intellectual content
of the resource
• Publisher: The publisher
• Contributor: A secondary contributor to
the intellectual content of the resource
The following example shows a more real and
complete scenario using the DC metadata. It can
be observed that more than one predicate-value
pair can be indicated for a resource. Basically,
it expresses that the resource “http://dme.uma.
pt/jcardoso” has the title “Jorge Cardoso Web
Page,” its subject is “Home Page,” and was cre-
ated by “Jorge Cardoso.”
The graphic representation of Figure 8 is
expressed in RDF using the DC namespace with
the following statements:
99900#”
xmlns:DC = “ http://dublincore.org/00/0//
dces#”
Description about = “http://dme.uma.pt/jcardoso/”
DC:Title Jorge Cardoso Home Page /DC:
Title
DC:Date 00-0- /DC:Date
/Description
/RDF
Verygoodexamplesofrealworldsystemsthat
useRDFaretheapplicationsdevelopedunderthe
Mozilla project (Mozilla, 2005). Mozilla soft-
ware applications use various different pieces of
structured data, such as bookmarks, file systems,
documents, and sitemaps. The creation, access,
query, and manipulation code for these resources
is completely independent. While the code is
completely independent, there is considerable
overlap in the data model used by all these dif-
ferent structures. Therefore, Mozilla uses RDF
to build a common data model shared by various
applications, such as viewers, editors, and query
mechanisms.
rdf schema
The RDF schema (RDFS, 2004) provides a type
system for RDF. The RDFS is technologically
advanced compared to RDF since it provides a
way of building an object model from which the
actual data is referenced and which tells us what
things really mean.
http://dme.uma.pt/jcardoso/ Home Page
DC:Subject
resource
Property type Property value
Jorge Cardoso Web Page
DC:Title
Jorge Cardoso
DC:Creator
Figure 8. Graphic representation of a RDF statement

Briefly, the RDF schema (RDFS) allows users
to define resources with classes, properties, and
values. The concept of RDF class is similar to the
concept of class in object-oriented programming
languagessuchasJavaandC++.Aclassisastruc-
ture of similar things and inheritance is allowed.
This allows resources to be defined as instances
ofclasses,andsubclassesofclasses.Forexample,
the RDF schema allows resources to be defined
as instances of one or more classes. In addition,
it allows classes to be organized in a hierarchical
fashion. For example the class First_Line_Man-
ager might be defined as a subclass of Manager
which is a subclass of Staff, meaning that any
resource which is in class Staff is also implicitly
in class First_Line_Manager as well.
An RDFS property can be viewed as an at-
tribute of a class. RDFS properties may inherit
from other properties, and domain and range
constraints can be applied to focus their use. For
example, a domain constraint is used to limit
whatclassorclassesaspecificpropertymayhave
and a range constraint is used to limit its possible
values.Withtheseextensions,RDFScomescloser
to existing ontology languages. RDFS is used to
declare vocabularies, the sets of semantics prop-
erty-types defined by a particular community.
As with RDF, the XML namespace mechanism
serves to identify RDFS. The statements in Box
1illustrateaverysimpleexampleofRDFSwhere
classes and inheritance are used.
The rdfs:Class is similar to the notion of a
classinobject-orientedprogramminglanguages.
When a schema defines a new class, the resource
representing that class must have an rdf:type
property whose value is the resource rdfs:Class.
AnythingdescribedbyRDFexpressionsiscalled
a resource and is considered to be an instance of
the class rdfs:Resource. Other elements of RDFS
are illustrated in Figure 9 and described below.
• rdfs:Datatype is the class of data types and
defines the allowed data types.
• rdfs:Literal is the class of literal values such
as strings and integers.
• rdfs:subClassOf is a transitive property that
specifies a subset-superset relation between
classes.
• rdfs:subPropertyOf is an instance of rdf:
Property used to specify that one property
is a specialization of another.
• rdfs:comment is a human-readable descrip-
tion of a resource.
• rdfs:label is a human-readable version of a
resource name and it can only be a string
literal.
• rdfs:seeAlso specifies a resource that might
provide additional information about the
subject resource.
• rdfs:isDefinedBy is a subproperty of rdfs:
seeAlso and indicates the resource defining
the subject resource.
• rdfs:member is a super-property of all the
container membership properties
• rdfs:range indicates the classes that the
values of a property must be members of.
?xml version=”.0”?
rdf:RDF
xmlns:rdf= “http://www.w.org/999/0/-rdf-syn-
tax-ns#”
xmlns:rdfs=”http://www.w.org/000/0/rdf-sche-
ma#”
xml:base= „http://www.hr.com/humanresources#“
rdf:Description rdf:ID=”staff”
rdf:type
rdf:resource=http://www.w.org/000/0/rdf-
schema#Class/
/rdf:Description
rdf:Description rdf:ID=manager
rdf:type
rdf:resource=http://www.w.org/000/0/rdf-
schema#Class/
rdfs:subClassOf rdf:resource=#staff/
/rdf:Description
/rdf:RDF
class
subclass of
class
Box 1.

• rdfs:domain indicates the classes on whose
member a property can be used.
• rdfs:Container is a collection of resources.
• rdfs:ContainerMemberShipProperty is a
class that is used to state that a resource is
a member of a container.
ontologies
An ontology is an agreed vocabulary that pro-
vides a set of well-founded constructs to build
meaningfulhigherlevelknowledgeforspecifying
the semantics of terminology systems in a well
definedandunambiguousmanner.Foraparticular
domain, an ontology represents a richer language
for providing more complex constraints on the
typesofresourcesandtheirproperties.Compared
to a taxonomy, ontologies enhance the semantics
oftermsbyprovidingricherrelationshipsbetween
the terms of a vocabulary. Ontologies are usually
expressed in a logic-based language, so that de-
tailed and meaningful distinctions can be made
among the classes, properties, and relations.
Ontologies can be used to increase commu-
nication either between humans and computers.
The three major uses of ontologies (Jasper
Uschold, 1999) are:
• To assist in communication between hu-
mans.
• Toachieveinteroperabilityandcommunica-
tion among software systems.
• To improve the design and the quality of
software systems.
In the previous sections, we have established
thatRDF/Swasoneofthebasemodelsandsyntax
forthesemanticWeb.OnthetopoftheRDF/Slayer
itispossibletodefinemorepowerfullanguagesto
describe semantics. The most prominent markup
language for publishing and sharing data using
ontologies on the Internet is the Web Ontology
Language(OWL,2004).WebOntologyLanguage
(OWL) is a vocabulary extension of RDF and is
derivedfromtheDAML+OILlanguage(DAML,
2001), with the objective of facilitating a better
machineinterpretabilityofWebcontentthanthat
supported by XML and RDF. OWL adds a layer
of expressive power to RDF/S, providing power-
ful mechanisms for defining complex conceptual
structures, and formally describes the semantics
of classes and properties used in Web resources
using,mostcommonly,alogicalformalismknown
as description logic (DL, 2005).
Let’sanalyzesomeofthelimitationsofRDF/S
to identify the extensions that are needed:
1. RDF/Scannotexpressequivalencebetween
properties. This is important to be able
to express the equivalence of ontological
concepts developed by separate working
groups.
2. RDF/S does not have the capability of ex-
pressing the uniqueness and the cardinality
of properties. In some cases, it may be nec-
essary to express that a particular property
valuemayhaveonlyonevalueinaparticular
class instance.
3. RDF/Scanexpressthevaluesofaparticular
property but cannot express that this is a
closed set. For example, an enumeration for
Figure 9. Relationships between the concepts of
RDF schema

6
the values for the gender of a person should
have only two values: male and female.
4. RDF/S cannot express disjointedness. For
example, the gender of a person can be male
or female. While it is possible in RDF/S
to express that John is a male and Julie a
female, there is no way of saying that John
is not a female and Julie is not a male.
5. RDF/Scannotexpresstheconceptofunions
and intersections of classes. This allows the
creationofnewclassesthatarecomposedof
other classes. For example, the class “staff”
might be the union of the classes “CEO,”
“manager,” and “clerk.” The class “staff”
may also be described as the intersection
of the classes “person” and “organization
employee.”
Let us see a more detailed example of RDF/S
limitations. Consider the sentence:
There are three people responsible for the Web
resource ‘Jorge Cardoso Home Page’created in
23 July 2005: Web designer, editor, and graphic
designer. Each has distinct roles and responsi-
bilities.
Using RDF/S we could try to model this state-
ment in the following way:
99900#”
xmlns:DC = “ http://dublincore.org/00/0//
dces#”
xmlns:S = “ http://hr.org/00/0//hr#”
Description about = “http://dme.uma.pt/jcardoso/”
DC:Title Jorge Cardoso Home Page /DC:Title
DC:Date 00-0- /DC:Date
S:Administrator
rdf:Bag
rdf:li resource=”Web designer”/
rdf:li resource=”Editor”/
rdf:li resource=”Graphic designer”/
/rdf:Bag
/S:Administrator
/Description
/RDF
Inthisexamplewehaveusedthebagcontainer
model.InRDF,thecontainermodelisrestrictedto
threecomponents:bags,sequence,andalternative.
Bags are an unordered list of resources or liter-
als. A sequence is an ordered list of resources or
literals.Finally,alternativeisalistofresourcesor
literals that represent alternatives for the (single)
value of a property.
Using any of the three different relationships
in RDF, we are only able to explain the informa-
tion about the resources, but we cannot explain
the second part of our statement, that is, “Each
has distinct roles and responsibilities.”
Using OWL, we can represent the knowledge
associated with the second part of our statement
as shown below.
owl:AllDifferent
owl:distinctMembers rdf:parse Type=”Collection”
admin:Administrator rdf:about=”#Web designer”/
admin:Administrator rdf:about=”#Editor”/
admin:Administrator rdf:about=”#Graphic designer”/
/owl:distinctMembers
/owl:AllDifferent
The owl:AllDifferent element is a built-in
OWL class, for which the property owl:distinct-
Members is defined, which links an instance
of owl:AllDifferent to a list of individuals. The
intended meaning of such a statement is that the
individuals in the list are all different from each
other. This OWL representation can express that
the three administrators (Web designer, Editor,
and Graphic designer) have distinct roles. Such
semanticscannotbeexpressedusingRDF,RDFS,
or XML.
logic, Proof, and trust
The purpose of this layer is to provide similar
features to the ones that can be found in first
order logic (FOL). The idea is to state any logical
principle and allow the computer to reason by
inference using these principles. For example,
a university may decide that if a student has a
GPA higher than 3.8, then he will receive a merit

scholarship. A logic program can use this rule
to make a simple deduction: “David has a GPA
of 3.9, therefore he will be a recipient of a merit
scholarship.”
Inference engines, also called reasoners, are
software applications that derive new facts or as-
sociations from existing information. Inference
and inference rules allow for deriving new data
fromdatathatisalreadyknown.Thus,newpieces
of knowledge can be added based on previous
ones. By creating a model of the information
and relationships, we enable reasoners to draw
logical conclusions based on the model. The use
of inference engines in the semantic Web allows
applicationstoinquirewhyaparticularconclusion
has been reached, that is, semantic applications
cangiveproofoftheirconclusions.Prooftracesor
explains the steps involved in logical reasoning.
For example, with OWL it is possible to make
inferences based on the associations represented
in the models, which primarily means inferring
transitive relationships. Nowadays, many infer-
ence engines are available. For instance:
• JenaReasoner:Jenaincludesagenericrule
basedinferenceenginetogetherwithconfig-
ured rule sets for RDFS and for OWL. It is
an open source Java framework for writing
semanticWebapplicationsdevelopedbyHP
Labs (Jena, 2005).
• Jess: Using Jess (Gandon Sadeh, 2003)
it is possible to build Java software that has
the capacity to “reason” using knowledge
supplied in the form of declarative rules.
Jess has a small footprint and it is one of
the fastest rule engines available. It was
developed at Carnegie Melon University.
• SWI-PrologSemanticWebLibrary:Pro-
log is a natural language for working with
RDF and OWL. The developers of SWI-
Prolog have created a toolkit for creating
and editing RDF and OWL applications, as
well as a reasoning package (Wielemaker,
2005).
• FaCT++:Thissystemisadescriptionlogic
reasoner,whichisare-implementationofthe
FaCT reasoner. It allows reasoning with the
OWL language (FaCT, 2005).
Trust is the top layer of the Semantic Web
architecture. This layer provides authentication
of identity and evidence of the trustworthiness of
data and services. While the other layers of the
semantic Web stack have received a fair amount
of attention, no significant research has been car-
ried out in the context of this layer. The idea is
to allow people to ask questions concerning the
trustworthiness of the information on the Web.
Possible scenarios for the trust layer include the
possibility to make statements such as “I trust
all information from http://dme.uma.pt/jcardoso,
but I don’t trust anything from http://www.inter-
netsite.com.”
aPPlications of thE
sEMantic WEb
Even though the Semantic Web is still in its in-
fancy,therearealreadyapplicationsandtoolsthat
use this conceptual approach to build semantic
Web based systems. The intention of this section
is to present the state of the art of the applications
that use semantics and ontologies. We describe
various applications ranging from the use of
semantic Web services, semantic integration of
tourisminformationsources,andsemanticdigital
libraries to the development of bioinformatics
ontologies.
semantic Web services
Web services are modular, self-describing,
self-contained applications that are accessible
over the Internet (Curbera, Nagy, et al., 2001).
Currently, Web services are described using the
Web Services Description Language (Chinnici,
Moreau, et al., 2006), which provide operational

information.AlthoughtheWebServicesDescrip-
tionLanguage(WSDL)doesnotcontainsemantic
descriptions, it specifies the structure of message
components using XML schema constructs. One
solution to create semantic Web services is by
mapping concepts in a Web service description
(WSDL specification) to ontological concepts
(LSDIS, 2004). The WSDL elements that can be
marked up with metadata are operations, mes-
sages, and preconditions and effects, since all
the elements are explicitly declared in a WSDL
description. Approaches and initiatives which
goal is to specify Web Services using semantics
and ontologies include OWL-S (OWL-S, 2004),
SWSI (SWSI, 2004), SWWS (SWWS, 2004),
WSML (WSML, 2004), WSMO (WSMO, 2004),
WSMX(WSMX,2004),andWSDL-S(Akkiraju,
Farrell, et al., 2006)
semantic tourism information
systems
Dynamic packaging technology helps online
travel customers to build and book vacations. It
can be described as the ability for a customer to
puttogetherelementsofa(vacation)tripincluding
flights, hotels, car rentals, local tours and tickets
totheatreandsportingevents.Thepackagethatis
created is handled seamlessly as one transaction
andrequiresonlyonepaymentfromtheconsumer,
hiding the pricing of individual components. So
far, the travel industry has concentrated its ef-
forts on developing open specification messages,
based on XML, to ensure that messages can flow
betweenindustrysegmentsaseasilyaswithin.For
example, the OpenTravel Alliance (OTA, 2004)
is an organization pioneering the development
and use of specifications that support e-business
among all segments of the travel industry. It has
produced more than 140 XML-based specifica-
tions for the travel industry.
The development of open specification mes-
sages based on XML, such as OTA schema,
to ensure the interoperability between trading
partners and working groups is not sufficiently
expressive to guarantee an automatic exchange
andprocessingofinformationtodevelopdynamic
applications. A more appropriate solution is to
use technologies from the semantic Web, such
as ontologies, to deploy common language for
tourism-relatedterminologyandamechanismfor
promoting the seamless exchange of information
across all travel industry segments. Ontologies
are the key elements enabling the shift from a
purely syntactic to a semantic interoperability.
Anontologycanbedefinedastheexplicit,formal
descriptions of concepts and their relationships
that exist in a certain universe of discourse, to-
gether with a shared vocabulary to refer to these
concepts.Withrespecttoanontologyaparticular
user group commits to, the semantics of data
provided by the data sources to be integrated can
bemadeexplicit.Ontologiescanbeappliedtothe
area of dynamic packaging to explicitly connect
data and information from tourism information
systems to its definition and context in machine-
processable form.
semantic digital libraries
Libraries are a key component of the informa-
tion infrastructure indispensable for education.
They provide an essential resource for students
and researchers for reference and for research.
Metadata has been used in libraries for centuries.
For example, the two most common general clas-
sification systems, which use metadata, are the
Dewey Decimal Classification (DDC) system
andtheLibraryofCongressClassification(LCC)
system. The DDC system has 10 major subjects,
each with 10 secondary subjects (DDC, 2005).
The LCC system uses letters instead of numbers
to organize materials into 21 general branches of
knowledge. The 21 subject categories are further
divided into more specific subject areas by add-
ing one or two additional letters and numbers
(LCCS, 2005).

9
As traditional libraries are increasingly con-
verting to digital libraries, a new set of require-
ments has emerged. One important feature of
digital libraries is the ability to efficiently browse
electronic catalogues browsed. This requires the
useofcommonmetadatatodescribetherecordsof
the catalogue (such as author, title, and publisher)
and common controlled vocabularies to allow
subject identifiers to be assigned to publications.
The use of a common controlled vocabulary,
thesauri, and taxonomy (Smrz, Sinopalnikova
et al., 2003) allows search engines to ensure
that the most relevant items of information are
returned. Semantically annotating the contents
of a digital library’s database goes beyond the
use of a controlled vocabulary, thesauri, or tax-
onomy. It allows retrieving books’ records using
meaningful information to the existing full text
and bibliographic descriptions.
Semantic Web technologies, such as RDF
and OWL, can be used as a common interchange
formatforcataloguemetadataandsharedvocabu-
lary, which can be used by all libraries and search
engines(Shum,Mottaetal.,2000)acrosstheWeb.
This is important since it is not uncommon to
find library systems based on various metadata
formats and built by different persons for their
specialpurposes.Bypublishingontologies,which
can then be accessed by all users across the Web,
library catalogues can use the same vocabular-
ies for cataloguing, marking up items with the
most relevant terms for the domain of interest.
RDF and OWL provide a single and consistent
encoding system so that implementers of digital
library metadata systems will have their task
simplified when interoperating with other digital
library systems.
semantic Grid
The concept of Grid (Foster Kesselman, 1999)
has been proposed as a fundamental computing
infrastructure to support the vision of e-Science.
The Grid is a service for sharing computer power
and data storage capacity over the Internet and
goes well beyond simple communication provid-
ing functionalities that enable the rapid assembly
and disassembly of services into temporary
groups.
Recently, the Grid has been evolving towards
the Semantic Grid to yield an intelligent platform
which allows process automation, knowledge
sharing and reuse, and collaboration within a
community (Roure, Jennings, et al., 2001). The
Semantic Grid is about the use of semantic Web
technologiesinGridcomputing;itisanextension
of the current Grid. The objective is to describe
information,computingresources,andservicesin
standardwaysthatcanbeprocessedbycomputers.
Resources and services are represented using the
technologies of the semantic Web, such as RDF.
The use of semantics to locate data has important
implicationsforintegratingcomputingresources.
It implies a two-step access to resources. In step
one, a search of metadata catalogues is used to
find the resources containing the data or service
required by an application. In the second step, the
data or service is accessed or invoked.
semantic Web search
Swoogle (Swoogle, 2005) is a crawler-based in-
dexing and retrieval system for the semantic Web
built on top of the Google API. It was developed
in the context of a research project of the Ebiquity
researchgroupattheComputerScienceandElec-
trical Engineering Department of the University
ofMaryland,USA.IncontrasttoGoogle(Google,
2005), Swoogle discovers, analyzes, and indexes
SemanticWebDocuments(SWD)writteninRDF
and OWL, rather than plain HTML documents.
Documents are indexed using metadata about
classes, properties, and individuals, as well as
the relationships among them. Unlike traditional
search engines, Swoogle aims to take advantage
of the semantic metadata available in semantic
Web documents. Metadata is extracted for each
discovereddocumentandrelations(e.g.,similari-

0
ties) among documents are computed. Swoogle
also defines an ontology ranking property for
SWD which is similar to the pageRank (Brin
Page, 1998) approach from Google and uses
this information to sort search results. Swoogle
provides query interfaces and services to Web
users. It supports software agents, programs via
serviceinterfaces,andresearchersworkinginthe
semantic Web area via the Web interface.
semantic bioinformatic systems
The integration of information sources in the life
sciences is one of the most challenging goals of
bioinformatics (Kumar Smith, 2004). In this
area, the Gene Ontology (GO) is one of the most
significantaccomplishments.TheobjectiveofGO
is to supply a mechanism to guarantee the con-
sistent descriptions of gene products in different
databases. GO is rapidly acquiring the status of
a de facto standard in the field of gene and gene
productannotations(KumarSmith,2004).The
GO effort includes the development of controlled
vocabularies that describe gene products, estab-
lishing associations between the ontologies, the
genes, and the gene products in the databases,
and develop tools to create, maintain, and use
ontologies (see http://www.geneontology.org/).
GO has over 17,000 terms and it is organized in
threehierarchiesformolecularfunctions,cellular
components, and biological processes (Bodenre-
ider, Aubry, et al., 2005).
Another well-known life science ontology is
the microarray gene expression data (MGED)
ontology. MGED provides standard terms in the
form of an ontology organized into classes with
properties for the annotation of microarray ex-
periments (MGED, 2005). These terms provide
an unambiguous description of how experiments
were performed and enable structured queries
of elements of the experiments. The comparison
between different experiments is only feasible if
there is standardization in the terminology for
describing experimental setup, mathematical
post-processing of raw measurements, genes,
tissues, and samples. The adoption of common
standardsbytheresearchcommunityfordescrib-
ing data makes it possible to develop systems for
the management, storage, transfer, mining, and
sharingofmicroarraydata(Stoeckert,Causton,et
al., 2002). If data from every microarray experi-
mentcarriedoutbydifferentresearchgroupswere
storedwiththesamestructure,inthesametypeof
database, the manipulation of data would be rela-
tively easy. Unfortunately, in practice, different
researchgroupshaveverydifferentrequirements
and, therefore, applications need mappings and
translationsbetweenthedifferentexistingformats
(Stoeckert, Causton, et al., 2002).
conclusion
Since its creation, the World Wide Web has al-
lowed computers only to understand Web page
layout for display purposes without having ac-
cess to their intended meaning. The semantic
Web aims to enrich the existing Web with a
layer of machine-understandable metadata to
enable the automatic processing of information
by computer programs. The semantic Web is not
a separate Web but an extension of the current
one, in which information is given well-defined
meaning, better enabling computers and people
to work in cooperation. To make possible the
creation of the semantic Web the W3C (World
WideWebConsortium)hasbeenactivelyworking
on the definition of open standards, such as the
RDF and OWL, and incentivate their use by both
industry and academia. These standards are also
important for the integration and interoperability
for intra- and inter-business processes that have
become widespread due to the development of
business-to-business and business-to-customer
infrastructures.
The Semantic Web does not restrict itself to
theformalsemanticdescriptionofWebresources
for machine-to-machine exchange and auto-

matedintegrationandprocessing.Oneimportant
feature of formally describing resources is to
allow computers to reason by inference. Once
resources are described using facts, associations,
and relationships, inference engines, also called
reasoners, can derive new knowledge and draw
logical conclusions from existing information.
The use of inference engines in the semantic
Web allows applications to inquire why a par-
ticular logical conclusion has been reached, that
is, semantic applications can give proof of their
conclusions by explaining the steps involved in
logical reasoning.
Even though the semantic Web is still in its
infancy, there are already applications and tools
that use this conceptual approach to build seman-
tic Web based systems, ranging from the use of
semantic Web services, semantic integration of
tourisminformationsources,andsemanticdigital
libraries to the development of bioinformatics
ontologies.
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EndnotE
1
http://fwrlibrary.troy.edu/1/dbhelp/dbhelp-
psychology.htm

Chapter II
Logics for the Semantic Web
Jos de Bruijn
University of Innsbruck, Austria
Copyright © 2007, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
abstract
This chapter introduces a number of formal logical languages which form the backbone of the Seman-
tic Web. They are used for the representation of both ontologies and rules. The basis for all languages
presented in this chapter is the classical first-order logic. Description logics is a family of languages
which represent subsets of first-order logic. Expressive description logic languages form the basis for
popular ontology languages on the Semantic Web. Logic programming is based on a subset of first-
order logic, namely Horn logic, but uses a slightly different semantics and can be extended with non-
monotonic negation. Many Semantic Web reasoners are based on logic programming principles and
rule languages for the Semantic Web based on logic programming are an ongoing discussion. Frame
Logic allows object-oriented style (frame-based) modeling in a logical language. RuleML is an XML-
based syntax consisting of different sublanguages for the exchange of specifications in different logical
languages over the Web.
introduction
An important property of the Semantic Web is
that the information contained in it is specified
using a formal language in order to enable ma-
chine-processability and the derivation of new
knowledge from existing knowledge. Logical
languages are such formal languages.
Using logical languages for knowledge rep-
resentation allows one to derive new knowledge
which is implicit in existing descriptions. Ad-
ditionally, the use of formal languages allows
one to write unambiguous statements and allows
machines to derived implicit information using
formal rules of deduction associated with the
language. Finally, logical languages have been
extensively studied in the research areas of da-
tabases and artificial intelligence. It is for these
reasons that logical languages form the backbone
of the Semantic Web.

Logical languages can be used for the repre-
sentation of different kinds of knowledge, most
notably ontologies and rules. In this chapter we
describe a number of logical languages which are
being used for the representation of knowledge
on the Semantic Web.
Classical first-order logic (Fitting, 1996) is
the basis for all the languages we survey in this
chapter. Full first-order logic by itself is a very
expressive language. In fact, the language is so
expressive that reasoning with the language is in
general very hard and the most interesting prob-
lems are undecidable. The answer to a question
such as “Does sentence φ follow from theory Φ?”
cannotalwaysbefound.Forthesereasons,several
subsetsoffirst-orderlogichavebeeninvestigated
andformthebasisforseverallanguageswhichare
used on the Semantic Web, most notably descrip-
tion logics and logic programming. Nonetheless,
full first-order logic has been proposed as a lan-
guage for the Semantic Web (Battle, Bernstein,
Boley,Grosof,Gruninger,Hull,2005;Horrocks,
Patel-Schneider, Boley, Tabet, Grosof, Dean,
2004; Patel-Schneider, 2005).
Description logics (Baader, Calvanese, Mc-
Guinness, Nardi, Patel-Schneider, 2003) are a
family of languages which generally represent
strictsubsetsoffirst-orderlogic.Descriptionlogics
wereoriginallydevisedtoformalizeframe-based
knowledge representation systems. Languages
in this family typically allow the definition of
concepts, concept hierarchies, roles and certain
restrictions on roles. Description logics receive a
lot of attention as a basis for ontology languages
on the Semantic Web; most notably, the W3C
recommendation OWL is based on an expressive
description logic (Horrocks, Patel-Schneider,
Harmelen, 2003).
Logic programming (Lloyd, 1987) is based on
the Horn logic subset of first-order logic, which
allows one to write rules of the form “if A then
B”. In order to allow for efficient reasoning, the
semantics of logic programming is built around
Herbrand interpretation, rather than first-order
interpretations.Logicprogrammingisbeingused
as an implementation platform for the Semantic
Web, but has also been proposed as the basis for
ruleandontologylanguagesontheSemanticWeb
(Angele, Boley, Bruijn, Fensel, Hitzler, Kifer,
2005; Battle et al., 2005).
Frame logic (Kifer, Lausen, Wu, 1995) is
an extension of first-order logic which allows an
object-oriented (frame-based) style of modeling.
Frame logic does not increase the theoretical ex-
pressivenessoffirst-orderlogic,butallowsamore
convenient style of modeling. F-Logic program-
ming is a subset of Frame Logic which extends
logic programming with frame-based modeling
primitives; in this chapter, we will restrict our-
selves to this subset. F-Logic programming has
been proposed as a basis for ontology and rule
languages for the Semantic Web (Kifer, 2005).
In this chapter we describe each of these
languages from the point-of-view of knowledge
representation; that is, we describe which kind of
knowledge can be described using the language.
We also mention certain complexity results
for reasoning with these languages, but do not
describe the reasoning procedures in detail. Ad-
ditionally, we describe the RuleML XML syntax
for exchange of rules and logical specification in
general over the Web.
first-ordEr loGic
Thebasicbuildingblocksoffirst-orderlogic(FOL)
are constants, function symbols and predicates.
Constants are interpreted as objects in some ab-
stract domain. Function symbols are interpreted
asfunctionsandpredicatesareinterpretedasrela-
tions over the domain. The domain may consist
of objects representing such things as numbers,
persons, cars, and so forth. The relations may be
such things as “greater-than,” “marriage,” “top
speed,” and so forth. Constants, predicates and
functionsymbolsarecombinedwithvariablesand
logical connectives to obtain formulas. We want

6
to interpret such formulas as assertions. Whether
such an assertion is true of false depends on the
context, that is, on the choice of the domain.
Inthissectionwewillfirstdefinehowformulas
and theories in a first-order language are created
from terms, predicates and a number of logical
connectives.Wewillthendefineinterpretationsof
first-orderformulasandtheories,anddefinewhen
an interpretation is a model of a given formula
or theory. The set of models defines the actual
meaning, or semantics, of a theory. Using the
definition of a model, we define entailment, that
is,thequestionwhetheraformulalogicallyfollows
from a theory. For a more detailed treatment of
first-order logic, included methods for automated
theorem proving, see (Fitting, 1996).
formulas and theories
The signature Σ of a first-order language L, also
referred to as first-order signature, consists of
countable sets C, F, P and V of constant, function,
predicate and variable symbols, respectively.
Each function symbol f∈F and each predicate
symbol p∈P has an associated arity n, which is
a non-negative integer.
Definition 1 (Terms) We define the set of terms of
the language L as follows:
• every constant c∈C is a term in L,
• every variable x∈V is a term in L,
• if f∈F is an n-ary function symbol and
(t1
,...,tn
) are terms in L, then f(t1
,...,tn
) is a
term in L.
A ground term is a term with no variables.
Example 1 Given the signature S=C,F,P,V with
theconstantsC={a,b},functionsymbolsF={f,g},
both with arity 1, predicate symbols P={p,q,r},
where p and q have arity 2 and r has the arity
1, and variables V={x,y,z}, then the following
are examples of terms: x,b,f(a),g(f(a)),g(y). Fur-
thermore, b,f(a),g(f(a)) are examples of ground
terms.
An atomic formula is either a predicate ex-
pression of the form p(t1
,...,tn
) where p is an n-ary
predicate symbol in L and (t1
,...,tn
) are terms in L,
one of the propositional constants ⊥, T or t1
=t2
,
wheret1
,t2
aretermsinL.Agroundatomicformula
is an atomic formula with no variables.
Example 2 Give the signature S as in the pre-
vious example, then the following are atomic
formulas:
p(a,b),p(x,f(g(y))),q(f(a),b),r(g(f(a))),r(z),a=f(b),
f(x)=f(g(y)), ⊥
Of these,
p(a,b),q(f(a),b),r(g(f(a))),a=f(b), ⊥
are ground atomic formulas.
Definition2(Formulas)Giventheformulasφ,ψ∈
L, we define the set of formulas in L as follows:
• every atomic formula is a formula in L,
• ¬φ is a formula in L,
• (φ∧ψ) is a formula in L,
• (φ∨ψ) is a formula in L,
• (φ→ψ) is a formula in L,
• given a variable x∈V, ∃x.(φ) is a formula in
L,
• given a variable x∈V, ∀x.(φ) is a formula in
L.
A variable occurrence is called free if it does
not occur in the scope of a quantifier (∃,∀). A
formulaisopenifithasfreevariableoccurrences.
A formula is closed if it is not open. A closed
formula is also called a sentence of L.
Example 3 Give the signature Σ as before, then
the following are sentences of L:

• ∃x.(∀y.(p(x,y)∧q(f(a),x)→r(y)))
• (p(a,b)∨¬r(f(b)))∨∃z.(q(z,f(z))
The following is an example of an open for-
mula:
∃x.(p(x,y))→r(y)))
Example 4 Let’s consider the sentences:
• All humans are mortal
• Socrates is a human
This can be written in first-order logic as
follows:
∀x.(human(x)→mortal(x))
human(Socrates)
Intuitively, these sentences can be read as:
• “For all objects it is the case that if they
have the property ‘human’, they have the
property ‘mortal’.”
• “The object ‘socrates’ has the property ‘hu-
man’.”
A first-order language L consists of all the
formulas which can be written using its signature
Σ according to Definition 2. A first-order theory
Φ of a first-order language L is a set of formulas
such that Φ ⊆ L.
interpretations, Models, and
Entailment
Thesemantics(ormeaning)ofafirst-ordertheory
is defined by a set of interpretations. In particu-
lar, by all interpretations in which the theory is
true. Thus, in a sense, the meaning of a theory is
constrainedbyalltheinterpretationswhichmake
the theory true. It follows that a first-order theory
does not say what is true in a particular world,
or interpretation, but rather limits the number of
possible worlds which may be considered. We
now give a formal definition.
Definition 3 (Interpretation) An interpretation
for a language L is a tuple w=〈U,I〉, where U is a
nonemptyset,calledthedomainoftheinterpreta-
tion and I is a mapping which assigns:
• an element cI
∈U to every constant symbol
c∈C,
• a function fI
:Un
→U to every n-ary function
symbol f∈F, and
• a relation pI
⊆Un
, to every n-ary predicate
symbol p∈P.
A variable assignment B is a mapping which
assignsanelementxB
∈Utoeveryvariablesymbol
x∈V. A variable assignment B’ is an x-variant of
B if for every variable y∈V such that y≠x.
We are now ready to define the interpretation
of terms.
Definition 4 Given interpretation w=〈U,I〉, vari-
able assignment B, and a term t of L, we define
tw,B
as follows:
• for every constant symbol c∈C, cw,B
=cI
,
• for every variable symbol x∈V, xw,B
=xB
,
• if t=f(t1
,...,tn
), tw,B
=fI
(t1
w,B
,...,tn
w,B
).
We can see from Definition 4 that, given an
interpretation and a variable assignment, each
term is interpreted as one object in the domain.
We can now define satisfaction (truth) of first-
order formulas.
Definition 5 (Satisfaction) Let w=〈U,I〉 be an in-
terpretation for L, B a variable assignment, and
φ∈L a formula. We denote satisfaction of φ in w
(φ is true in w), given the variable assignment B,
with w |=B
φ . Satisfaction is recursively defined
as follows, with ψ, ψ1
, ψ2
formulas, p an n-ary
predicate symbol and t1
,...,tn
terms:

• w |=B
p(t1
,...,tn
) iff (t1
w,B
,...,tn
w,B
) ∈pI
,
• w |=B
⊥ and w |=B
T,
• w |=B
t1
=t2
iff t1
w,B
=t2
w,B
,
• w |=B
¬ψ iff w |≠B
ψ,
• w |=B
ψ1
∧ψ2
iff w |=B
ψ1
and w |=B
ψ2
,
• w |=B
ψ1
∨ψ2
iff w |=B
ψ1
or w |=B
ψ2
,
• w |=B
ψ1
→ψ2
iff whenever w |=B
ψ1
, w |=B
ψ2
,
• w |=B
∀x.(ψ) iff for every x-variant B' of B,
w |=B’
ψ,
• w |=B
∃x.(ψ) iff for some x-variant B' of B,
w |=B’
ψ.
A formula φ is satisfied by an interpretation
w, or φ is true in w, written as w |= φ, if w |=B
φ
for all variable assignments B. We that say w is
a model of φ if w |= φ.
If a formula has at least one model, we call the
formulasatisfiable;conversely,ifaformulahasno
models, it is unsatisfiable. We say that a formula
φ is valid if φ is true in every interpretation w of
L. An interpretation w is a model of a theory Φ⊆
L if w |= φ for every formula φ∈Φ.
Example 5 Consider the first-order language
L withtheconstantsymbola,theunaryfunction
symbol f, the binary function symbol g, and the
binary predicate p. Now consider the following
theory:
∀x.(g(a,x) = x)
∀x.∀y.(g(x,y) = g(y,x))
∀x.∀y.∀z.(g(x,g(y,z)) = g(g(x,y),z))
∀x.(p(x,x))
∀x.(p(x,f(x)))
∀x.∀y.∀z.(p(x,y)∧p(y,z)→p(x,z))
Nowconsidertheinterpretationw=〈N,I〉,with
the domain of the natural numbers N (including
0) and I assigns to a the number 0 (zero), to f
the successor function, that is, for every natural
number x, fI
(x) = x+1, and to g the addition opera-
tor, that is, for every pair of natural numbers x,y,
gI
(x,y)=x+y. Finally, I assigns to the predicate p
the relation ≤ (smaller-or-equal).
Now, the first formula is interpreted as
“0+x=x”, the second formula as “x+y=y+x” and
the third formula as “x+(y+z)=(x+y)+z”. The
fourth formula is interpreted as “x≤x” (reflexiv-
ity of ≤), the fifth as “x≤x+1” and the sixth as “if
x≤y and y≤z then x≤z” (transitivity of ≤). All these
statements are obviously true for the domain of
natural numbers, thus the theory is true in w and
w is a model for this theory.
The theory of the previous example is sat-
isfiable; the interpretation constructed in the
example is a model. It is not valid; one can easily
construct an interpretation which is not a model,
for example, any interpretation which assigns to
p an antireflexive relation.
An example of a valid formula is:
∀x.(p(x)∨¬p(x)).
It is easy to verify that in every interpretation
p(x)musteitherbetrueorfalseforeveryx,andthus
theformulaistrueineverypossibleinterpretation.
The following formula is unsatisfiable:
∃x.(p(x)∧¬p(x)).
It is easy to verify that in every interpretation
p(x) must either be true or false for every x; it
cannot be both. Therefore, p(x)∧¬p(x) cannot be
true for any x in any interpretation.
Definition 6 We say that a theory Φ⊆L entails a
formula φ∈L, denoted Φ |= φ, iff for all models
w of Φ, w |= φ.
We can reformulate this definition of entail-
ment using sets of models. Let Mod(Φ) denote
the set of models of some first-order theory Φ,
then we can reformulate entailment as set inclu-
sion: Mod(Φ)⊆Mod(φ) iff Φ |= φ. In a sense, the
entailing theory is more specific, that is, allows
fewer models, than the entailed theory.

9
We have characterized a first-order theory
Φ using its set of models Mod(Φ). Another way
of characterizing a first-order theory Φ is using
its set of entailments Ent(Φ). The set of entail-
ments of Φ is the set of all formulas which are
entailed by Φ: φ∈Ent(Φ) iff Φ |= φ. Now, the less
specific a theory is, the more models it has, but
the fewer entailments it has. We can observe
that a theory Φ entails a theory Ψ, that the set of
entailments of Φ is a superset of the entailments
of Ψ: Ent(Φ)⊇Ent(Ψ) iff Φ |= Ψ.
Example 6 Given the sentences p∧q and q, where
p,q are null-ary predicate symbols, then clearly:
p∧q |= q
because in all models where both p and q are true,
q must be true.
But not the other way around:
q |≠ p∧q
because there are models of q in which p is not
true.
In the example, p∧q presents a more con-
strained view of the world than q, namely both p
and q must be true, whereas q only mandates that
q must be true, but does not say anything about p.
Thus,thesetofmodelsofp∧q,Mod(p∧q),isasub-
set of the set of models of q: Mod(p∧q)⊆Mod(q),
because every model of p∧q is a model of q. It is
also easy to see that the set of entailments of p∧q,
Ent(p∧q), is larger than the set of entailments of
q: Ent(p∧q)⊃Ent(p). For example, p∧q entails
p∧q, whereas q does not.
It turns out that checking entailment in first-
order logic can be reduced to checking satisfi-
ability. In order to check whether some formula
φ is entailed by some theory Φ:
Φ |= φ
wecansimplyaddthenegationofφtoΦandcheck
whether this combination, Φ∪¬(φ) is satisfiable,
that is, has a model. If Φ∪¬(φ) is not satisfiable,
then the entailment holds.
Example7Considertheentailmentquestionfrom
the previous example:
p∧q |= q.
Wehaveconcludedearlierthatthisentailment
must hold, because every model of p∧q must be
a model of q. Now, let’s rewrite this entailment
problem to an unsatisfiability problem, that is, we
want to check whether:
{p∧q,¬q}
has a model. Clearly, this formula cannot have a
model, because both q and ¬q would have to be
true in this model and this is not possible. There-
fore, we can conclude that p∧q entails q.
We can now try to explain intuitively why
we can use unsatisfiability of Φ∪¬(φ) to check
entailment Φ |= φ. We have seen earlier that Φ
|= φ if and only if Mod(Φ)⊆Mod(φ). We know
that the sets of models of φ and ¬φ are disjoint,
because there can be no model in which both φ
and ¬φ are true.
Now, if Φ∪¬(φ) would be satisfiable, then
there would be one interpretation in which both
Φ and ¬φ are true. This means, by disjointness
of Mod(φ) and Mod(¬(φ)), that Φ has a model
which is not a model of Mod(φ), which means
that Mod(Φ) is not a subset of Mod(φ) and thus
Φ does not entail φ.
The satisfiability problem for first-order logic
is the problem to decide whether there is a model
for a given first-order theory Φ. In other words:
“Does Φ have a model? ”
It turns out that this question is not so easily
answered and in some cases it is even impossible
to find an answer. This makes the satisfiability
problem for first-order logic undecidable, that

0
is, the question of satisfiability cannot always be
answered in a finite amount of time. However, it
doesturnoutthatiftheanswertothesatisfiability
question is “yes”, the answer can always be found
in a finite amount of time. Therefore, first-order
logic is actually semi-decidable. It is possible to
enumerate all sentences which are entailed by a
first-order theory.
dEscriPtion loGics
Description logics (Baader et al., 2003) (formerly
called Terminological Logics) are a family of
knowledge representation languages, which
revolve mainly around concepts, roles (which
denote relationships between concepts), and role
restrictions.Descriptionlogicsareactuallybased
on first-order logic. Therefore, concepts can be
seen as unary predicates, whereas roles can be
seenasbinarypredicates.Althoughtherearealso
Description Logic languages which allow n-ary
roles, we will not discuss these here.
Inthissectionwewillillustratedescriptionlog-
ics through the relatively simple description logic
Attributive Language with Complement (ALC)
which allows concepts, concept hierarchies,
role restrictions and the boolean combination
of concept descriptions. The currently popular
expressive description logics, such as SHIQ and
SHOIQ, are all extensions of ALC.
the basic description logic ALC
AnALCknowledgebasehastwoparts:theTBox,
withterminologicalknowledge,whichconsistsof
anumberofclassdefinitions,andtheABox,which
consists of assertions about actual individuals.
Concept axioms in the TBox are of the form
C⊆D (meaning the extension of C is a subset of
the extension of D; D is more general than C) or
C≡D(whereC≡DisinterpretedasC⊆DandD⊆C)
with C and D (possibly complex) descriptions.
Descriptions can be built from named concepts
(e.g., A) and role restrictions (e.g., ∀R.C denotes
a universal value restriction), connected with
negation (¬), union (∪) and intersection (∩) (see
Box 1).
Traditionally, descriptions are interpreted as
sets, where the different parts of a description
constrain the set. Take, for example, the descrip-
tion A∩¬B∩∃R.C. This can be read as “all ele-
ments which are member of the set A, but not of
the set B (¬B); additionally, each member must
be related to some member of the set C via the
relation R (∃R.C)”.
Descriptions can also be understood as for-
mulas of first-order logic with one free variable.
For example, the description A∩¬B∩∃R.C cor-
respondstotheformulaA(x)∧¬B(x)∧∃y.(R(x,y)∧C
(y)). The correspondence between descriptions
and first-order formulas is given in Table 1. In the
table, π is a function which takes as parameters
a description and a variable and returns a first-
order formula.
The TBox of a description logic knowledge
base consists of a number of axioms of the forms
C⊆DandC≡D,whereCandDaredescriptions.In
theset-basedinterpretation,C⊆DmeansthatCis
interpreted as a subset of D and C≡D means that
C and D are interpreted as the same set. We give
the translation to first-order logic in Table 2.
Description logics have been devised to
formally model terminologies. The two major
benefits of the formal modeling of a terminology
C,D → A (named class)
T (universal concept)
⊥ (bottom concept)
C∩D (intersection)
C∪D (union)
¬C (negation)
∃R.C (existential restriction)
∀R.C (universal restriction)
Box 1.

are that (1) it is possible to verify the consistency
of the specification and (2) it is possible to auto-
maticallyinferinformationwhichishiddeninthe
terminology. We illustrate both in the following
example.
Example 8 Consider the following TBox T:
Person ≡ ∀hasChild.Person∩∃hasFather.
Father∩∃hasMother.Mother
Person ≡ Man∪Woman
Parent ≡ ∃hasChild.T
Mother ≡ Woman∩Parent
Father ≡ Μan∩Parent
Thastwoalternativedefinitionsoftheconcept
Person: (1) everyone who has a father which is
a father, has a mother which is a mother and has
only children which are persons (notice that this
does not require a person to have children) and
(2) the union of the sets of all men and women;
note that both definitions must be valid. A parent
is a person who has a child. A mother is a woman
who is also parent and a father is a man who is
also a parent.
The TBox T does not contain any inconsisten-
cies. We can infer some information from T. For
example: Man is subsumed by (is a more specific
concept than) Person: Man⊆Person.
We can see from T that every Man must be
a Person. Therefore, it would be inconsistent to
state that there is any Man who is not a Person.
We can add the following axiom to T:
ManNotPerson≡Man∩¬Person
A concept in a TBox is inconsistent if it is
impossible for this concept to have any instances,
that is, the concept can only be interpreted as the
empty set. In order to verify this, we translate the
second and the last axiom of T to first-order logic
to obtain the theory π(T):
∀x.(Person(x)→(Man(x)∨Woman(x)))
∀x.((Man(x)∨Woman(x))→Person(x))
∀x.(ManNotPerson(x)→(Man(x)∧¬Person(x)))
∀x.((Man(x)∧¬Person(x))→ManNotPerson(x))
The second formula says that every man is
a person, whereas the third formula says that
every ManNotPerson is a man and not a person.
This would be impossible, because by the second
formula every man is necessarily a person. What
followsisthateverymodelofthistheoryinterprets
ManNotPerson as the empty set.
IfweweretoaddtheformulaManNotPerson(a)
to π(T), for some constant a, then the theory no
longer has any model and is thus inconsistent.
Besides the concepts and role descriptions,
which comprise the TBox, a Description Logic
Knowledgebasetypicallyalsocontainsindividu-
als (instances) and relations between individuals
and, in the case of OWL, equality and inequality
Description First-Order Formula
π(C⊆D) ∀x.(π(C,x)→π(D,x))
π(C≡D) ∀x.(π(C,x)→π(D,x) ∧(π(D,x)→π(C,x))
Table 2. Correspondence between TBox axioms
and first-order formulas
Description First-Order Formula
π(A,X) A(X)
π(T,X) T
π(⊥,X) ⊥
π(C∩D,X) π(C,X)∧π(D,X)
π(C∪D,X) π(C,X)∨π(D,X)
π(¬C,X) ¬(π(C,X))
π(∃R.C,X) ∃y.(R(X,y)∧π(C,y))
π(∀R.C,X) ∀y.(R(X,y)→π(C,y))
Table 1. Correspondence between descriptions
and first-order formulas

Exploring the Variety of Random
Documents with Different Content

Ylimmäisellä rapulla seisova olento tuli neljä askelta vastaamme,
tervehti suurellisesti ja sanoi:
— Hra kreivi, me olemme tulleet tänne tänä muistopäivänä
osoittamaan teille kiitollisuuttamme...
Tällä kertaa oli se eräs belgialainen! Hänen jälkeensä koroitti
pienokainen äänensä kiittäen minua opetetulla ja sen vuoksi
teennäiseltä kuuluvalla kohteliaisuudella.
Tekeytyen viattomaksi pyysin minä neiti Elmiren hiukan sivulle ja
kysyin häneltä:
— Onko tuo lapsenne isä?
— Ei suinkaan, herra.
— Onko hänen isänsä siis kuollut?
--- Ei suinkaan, herra. Kyllä me joskus vieläkin tapaamme toisemme.
Hän on santarmi.
— Mitä sanotte? Tytön isä ei siis ollutkaan se marseillelainen, joka oli
seuralaisenne silloin synnytyksen aikana?
— Eihän toki. Se juoppolalli varasti minulta vähät säästönikin.
— Entäs santarmi, lapsen oikea isä, tunnustaako hän lapsensa?
— Kyllä, herra, vieläpä hän rakastaakin tytärtänsä. Mutta hän ei voi
ottaa tätä luoksensa, sillä hänellä on — muitakin lapsia ja oma
vaimo.

KUUTAMOLLA.
Pastori Marignan oli sotaisa mies. Hän oli pitkä, laiha, kiihkoisa ja
aina intomielinen, mutta rehellinen pappismies. Hänen uskonoppinsa
oli varma ja järkkymätön. Jumalansa kuvitteli hän tuntevansa
perinpohjaisesti ja uskoi pääsevänsä selville hänen
suunnitelmistansa, tahdostansa ja tarkoituksistansa.
Kävellessänsä pitkin askelin pienen maalaispappilansa puistotietä
liikkui hänen mielessänsä toisinaan kysymys: miksi on Jumala tämän
kaiken tehnyt? Asettuen ajatuksissansa Jumalan sijalle etsi hän
itsepintaisesti vastausta tekemäänsä kysymykseen ja löysikin sen
melkein aina. Hänelle ei johtunut mieleenkään hartaan nöyryyden
puuskauksessa huokaista: oi herra, tutkimattomat ovat sinun tiesi!
Hän mietti vain itseksensä: minä olen Herran palvelija, jonka tulee
tuntea syyt hänen menettelyynsä tai aavistaa ne, ellen niitä järjellä
käsitä.
Kaikki luonnossa näytti hänestä luodulta täydellisen ja ihmeteltävän
johdonmukaisesti. Kysymykset miksi ja vastaukset siksi, että
olivat aina tasapainossa. Aamurusko oli luotu ilahduttamaan
huomenkoittoa, aurinko kypsyttämään ja sade kostuttamaan viljaa,
ilta valmistautuaksemme uneen ja pimeä yö nukkuaksemme.
Neljä vuodenaikaa vastasivat täydellisesti maanviljelyksen kaikkia
tarpeita eikä pastorin mieleen ikinä olisi johtunut mitenkään epäillä,
että luonnolta puuttuu tarkoitusperää ja että kaikki elämä
päinvastoin taipuu ajan, ilman-alan ja aineen ankarien lakien
alaiseksi.
Mutta hän vihasi naisia, vihasi tietämättänsä ja halveksi niitä
vaistosta. Usein toisteli hän mielessänsä Kristuksen sanoja: vaimo,
mitä meillä on yhteistä, sinulla ja minulla? lisäten siihen, että
luultavasti oli luoja itsekin tyytymätön tähän luomistyöhönsä.

Nainen oli hänelle kymmenkertaisesti epäpuhdas lapsi, kuten eräs
runoilija sanoo. Hän oli kiusaaja, joka oli vietellyt ensimäisen
miespuolen ja yhä jatkoi kirottua työtänsä ollen muuten heikko,
vaarallinen ja salaperäisesti häiriötä aikaansaapa olento. Ja vielä
enemmän kuin naisen katoovaa ruumista, vihasi hän tämän
rakastuvaa sielua.
Usein oli hän tuntenut heidän hellästi liittyvän itseensä ja vaikka hän
tiesikin olevansa mahdoton hyökkäyksellä valloittaa, katkeroitui hän
tuosta rakkaudentarpeesta, joka heissä kaikissa asui väräjävänä
tunteena.
Hänen mielestänsä oli luoja luonut naisen ainoastaan miehen
kiusaukseksi ja koetukseksi. Naista ei voinut lähestyä muuten kuin
tarpeelliset puolustus-valmistukset tehtyänsä ja sittenkin oli heidän
paulojansa peljättävä. Ojennettuine käsivarsinensa ja miestä kohti
hymyilevine huulinensa olikin nainen vallan kuin joku viritetty ansa.
Ainoastaan uskonnollisia naisia kohtaan, joiden antama lupaus
Herralle teki heidät rauhallisiksi, oli hän mielessänsä anteeksi
antavainen; mutta sittenkin kohteli hän näitä kovaluontoisesti, koska
hän elävästi tunsi heidän kahlitun ja nöyryytetyn sydämensä
pohjassa ijankaikkisesti asuvan hellyyden, joka uhosi sieltä häntäkin
kohtaan, vaikka hän olikin pappismies. [Katoolisilla papeilla ei, kuten
tunnettu, ole oikeutta mennä naimisiin. Suoment. muist.]
Hän näki sen näiden hartaudesta raukeammissa katseissa kuin
munkkien oli, hän huomasi sen heidän kiihkoilustansa, jossa aina
ilmeni sukupuolista intoa ja heidän rakkauden purkauksistansa
Kristusta kohtaan, jotka suorastansa harmittivat häntä ainoastaan
sen vuoksi, että ne todistivat naisen rakkauden lihallisuutta. Samoin
näki hän tämän kirotun tunteenarkuuden ilmenevän yksin heidän
taipuisassa nöyryydessänsä, kuuli sen väräjävän heidän äänensä
vienoudessa ja huomasi sitä heidän maahan luoduissa silmissänsä ja
niissä kohtaloonsa alistuvissa kyynelissä, joita he vuodattivat aina
kun hän moitti heitä ankarammin.

Viskattuansa pois papillisen kauhtanansa poistui hän luostarikirkon
portista kulkien pitkin askelin ikäänkuin olisi hän paennut jotakin
vaaraa.
Pastori Marignanilla oli veljentytär, joka asui äitinsä kanssa eräässä
pienessä naapuritalossa. Tästä koetti hän kaikin voimin tehdä
laupeuden sisarta.
Tyttö oli kaunis, rajuluontoinen ja ivallinen. Pastorin saarnatessa hän
usein naureskeli ja kun pastori harmistui hänen käytökseensä, syleili
hän tätä rajusti puristaen häntä rintaansa vastaan, jolloin hän aina
koetti tahtomattansa vapautua tästä lujasta syleilystä, vaikka hän
silloin tunsikin sitä vienoa iloa, joka hänen sydämensä pohjassa
herätti tuon kaikissa miehissä uinailevan isyyden tunteen.
Tytön kanssa kyläpolkuja kävellessään puhui hän tälle usein
Jumalasta, siitä nimittäin, millaiseksi hän tämän oli ajatellut. Tyttö
tuskin kuuntelikaan häntä; katseli vain taivasta, heinikkoa ja
kukkasia silmistä säteilevällä elämänilolla. Joskus syöksyi hän
tavoittamaan jotakin lentävää elävää, jonka kiinni saatuansa palasi
enon luo huutaen: katsos, eno, kuinka kaunis se on; oikein tekisi
mieleni suudella sitä! Tämä hänen halunsa suudella hyönteisiä
tahi sireenin kukkasia harmitti, suututti ja kiihoitti pastoria, sillä
tässäkin näki hän tuon naisten sydämissä aina versovan hellyyden,
jota näytti olevan mahdoton hävittää.
Eräänä päivänä ilmoitti lukkarin leski, joka hoiti pastori Marignanin
taloutta, kaikella varovaisuudella, että hänen veljensä tyttärellä oli
rakastaja.
Pastori tunsi kauhistuvansa tästä uutisesta ja pysyi kotvan ikäänkuin
tukehtuneena unhottaen saippuan kasvoillensa, joita hän juuri oli
ajelemassa.
Saatuaan takaisin ajatus- ja puhekykynsä huudahti hän: se ei ole
totta! te valehtelette, Mélanie!
Mutta lukkarin emäntä laski käden sydämellensä sanoen:
tuomitkoon minut taivaallinen Jumala, jos valehtelen teille, hra
pastori. Minä vakuutan teille, että hän rientää lemmenkohtaukseen

joka ilta niin pian kuin sisarenne on mennyt levolle. He tapaavat
toisensa tuolla joen rannalla. Teidän tarvitsee vain mennä sinne
katsomaan illalla klo 10:n ja keskiyön välillä.
Pastori lakkasi raappimasta leukaansa ja alkoi kävellä rajusti
huoneessa, kuten hän aina teki jotakin tärkeää miettiessänsä. Ja kun
hän sitten aikoi ryhtyä uudelleen ajamaan partaansa, leikkasi hän
itseänsä kolme kertaa ... nenästä korvaan saakka.
Koko päivän pysyi hän mykkänä ja kuohui harmista ja
suuttumuksesta. Hänen papilliseen vihaansa tuota voittamatonta
rakkautta kohtaan liittyi tytön siveellisen isän, suojelijan ja
sielunpaimenen suuttumus siitä, että tuollainen lapsi oli voinut
pettää häntä ja tehdä hänelle moiset kepposet. Se oli tuota
itsekkäiden vanhempain suuttumusta sen johdosta, että tytöt
ilmoittavat valinneensa puolison itselleen heiltä kysymättä ja vastoin
heidän tahtoansa.
Päivällisen jälkeen koetti hän lukea hiukan, mutta ei voinut; kiukustui
vain yhä enemmän. Kun kello löi 10, otti hän keppinsä, peloittavan
tammisen sauvansa, jota hän aina käytti, kun hänen öisin täytyi
lähteä sairaitten luo. Hymähtäen katseli hän tuota hirmuisen suurta
myhkyrisauvaa, jota hän pyöritteli tukevassa maalaiskämmenessänsä
heilautellen sitä sangen uhkaavasti. Sitten nousi hän äkkiä ylös, puri
hammasta ja mätkäsi sillä erästä tuolia niin, että selkänoja
haljenneena lensi lattialle.
Jo avasi hän oven lähteäksensä ulos, mutta seisahtui kynnykselle
hämmästyneenä kovin harvinaisen kauniista kuutamosta.
Ollen kiihkosieluja, joita nuo runolliset uneksijat, vanhat kirkko-
isämme, nähtävästi ovat olleet, tunsi hän äkkiä itsensä hajamieliseksi
ja liikutetuksi kalpean yön suuremmoisesta ja kirkkaasta
kauneudesta.
Hänen pienessä puutarhassansa kylpi kaikki kuun vienossa
hohteessa; siinä seisoivat rivissä hedelmäpuut heittäen
puistokäytävälle varjot pitkistä, kapeista oksistansa, jotka tuskin vielä
vihersivät; jättimäiset kaprifoliot, jotka kiertelivät ylös talon

seinämiä, uhosivat hienoja ja hieman imeliä tuoksujansa täyttäen
koko valoisan ja haalean, ehtoisen ilman jonkunlaisella lemulla.
Pastori hengitti pitkään ... ahmien ilmaa kuin juopot viiniä ... ja asteli
virkistyneenä ja ihmeissänsä aivan verkkaisin askelin eteenpäin ...
melkein unhottaen veljensä tyttären.
Kedolle tultuansa pysähtyi hän katselemaan, kuinka koko lakeus
välkkyi tuota viehättävää hohdetta ja näytti ikäänkuin kylpevän
selkeän yön suloisessa ja vienossa viehkeydessä. Sammakkojen lyhyt
ja metallimainen kurnutus kuului alinomaa jonkun matkan päästä ja
tähän yhtyi kaukaisten satakielten keveä ja väräjävä liverrys, joka
saattaa meidät unelmoimaan, pakottaa meitä ajattelemaan elämän
kauneutta ja joka tuntuu ikäänkuin luodulta kuutamon houkutusta ja
vaihdettuja suudelmia varten.
Miksi kietoo Jumala maailman moiseen puoliharsoon? Miksi nämä
sydämen väristykset, nämä sielun liikutukset ja tämä lihan
heikontuva raukeneminen?
Miksi kaikki tämä laaja viehätys, josta ihmiset eivät kuitenkaan näe
mitään, kun makaavat vuoteissansa kaikessa rauhassa? Ketä varten
oli siis aiottu tämä yliluonnollinen näytäntö, tämä taivaasta maan
päälle viskattu runouden runsaus?
Pastori ei sitä ymmärtänyt.
Mutta silloin näkyi tuolla alhaalla joen rannalla, välkkyvään
valkoiseen usmaan kiedottujen puiden siimeskaaren alla, kahden
rinnakkain astelevan ihmisolennon varjot.
Mies oli hiukan pitempi naista jonka kaulan ympäri hän oli laskenut
kätensä ja jota hän tuon tuostakin otsalle suuteli. He loivat äkkiä
elon tähän liikkumattomaan luontoon, joka heidät ympäröi kuin heitä
varten luotu jumalallinen kehys. He näyttivät sulautuneen yhdeksi
olennoksi, jota ainoaa varten tämä vilpoisa, hiljainen yö oli aiottu ja
he lähestyivät sieltä kuin elävä luojan lähettämä vastaus pastorin
tekemään kysymykseen.

Sykkivin sydämin ja hämmästyneenä jäi hän seisomaan luullen
näkevänsä jonkun raamatullisen kuvan, joka muistutti Ruutin ja
Boasin rakkauden historiaa ja toteutti Jumalan tahdon sellaisessa
suuressa ihanuudessa, mistä pyhät kirjat tietävät kertoa. Hänen
päässänsä alkoi hymistä Korkean Veisun runolliset säkeet, kiihkeät
huudahdukset, vetoamiset lihan oikeuteen ja pastori lähti
kävelemään sydämen heikommin lyödessä, Jumala ties, mistä
syystä. Hän tunsi äkkiä itsensä heikoksi ja raukeaksi ja hänen teki
mielensä istua, jäädä siihen katselemaan ja ihailemaan luojaa hänen
luomistöissänsä.
Tuolla alhaalla, missä pieni joki kierteli kimaltelevana, seisoi mahtava
mutkitteleva poppelikujanne. Hieno valkea usva, jonka läpi kuun
hopeiset säteet välkkyvinä paistoivat, leijui rantarinteiden yli
kiehtoen koko joen mutkaisen uoman jonkunlaiseen kevyeen ja
kuultavaan harsoon.
Pastori pysähtyi vielä kerran tuntien sielunsa syvyydessä asti yhäti
kasvavaa ja vastustamatonta heltymistä.
Ja hänet valtasi epäilys ja epämääräinen levottomuus: hän tunsi
sydämessänsä syntyvän erään noita kysymyksiä, joita hän toisinaan
asetti itsellensä.
Miksi on luoja kaiken tämän tehnyt? kysyi hän. Jos yö kerran on
määrätty unta, tiedottomuutta, lepoa ja kaiken unhotusta varten,
niin miksi tehdä se päivää viehättävämmäksi, huomenkoittoa ja iltaa
suloisemmaksi, ja miksi sytyttää tuo hiljainen ja viehkeä tähtöinen
tuolla, joka näyttää aurinkoa runollisemmalta ja suorastaan
määrätyltä vienolla tuikkeellansa päivän sijasta valaisemaan hienoja
ja salaperäisiä asioita? Miksi sytyttää se tuota usvaharsoa noin
läpikuultavaksi tekemään?
Miksi ei tuo laululinnuista etevin livertelijä voi levätä niin kuin kaikki
muut linnut, vaan koroittaa äänensä tuollaisen samean usman
keskestä? Ja miksi koko tämän tulista tunnetta palavan laulun
huumaava runous?

Ja hän sanoi itseksensä, että ehkä on Jumala luonut tällaiset yöt
verhotaksensa ihmisten rakkauden jonkunlaisella ihanteellisuudella.
Hän väistyi syrjään tuon syleilevän ja häntä yhä lähestyvän
pariskunnan tieltä. Olihan se hänen oman veljensä tytär. Mutta hän
kysyi itseltänsä, eikö tuo sittenkin ollut vastoin Jumalan tahtoa. Vaan
kieltäisikö Hän rakkauden, joka varta vasten ympäröi sen tällaisella
ihanuudella?
Sangen liikutettuna ja melkeinpä häpeissään pakeni pastori pois
ikäänkuin olisi hän tunkeutunut temppeliin, johon hänellä ei ollut
oikeutta astua sisään.

VALLANKUMOUS.
Pariisiin oli juuri saapunut tieto Sedanin onnettomasta tappiosta.
Vallankumous oli julistettu. Koko Ranska huohotti hengästyneenä
tämän mielettömyyden edessä, jota kesti kommunikauden loppuun
saakka. Koko valtakunnassa leikittiin ajattelemattomasti sotilailla.
Sukkatehtailijat olivat everstejä ja hoitivat kenraalin tehtäviä; suuret,
rauhalliset vatsat olivat saaneet punaiset vyöt siteiksensä ja
komeilivat revolvereilla ja tikareilla; pikkuporvarit, joista tehtiin
tilapäisiä sotilaita, komensivat räyhääviä, vapaaehtoisia pataljooneja
kiroillen kuin kuorma-ajurit ... muka paremman ryhdin vuoksi.
Jo lupa saada kantaa aseita ja käsitellä pyssyjä järjestelmällisesti
hullutti näitä ihmisiä, jotka tähän saakka olivat käsitelleet ainoastaan
kauppakirjoja, ja saattoi heidät syyttömästi peloittaviksi ihmisten
silmissä. Sitten mestattiin viattomia ihmisiä näyttääksensä, että
osattiin tappaa, ja ammuskeltiin — preussilaisten hurmetta vielä
uhoavilla kentillä vaellettaessa — kulkukoiria, rauhassa märehtiviä
lehmiä ja heinikoissa laitumella käyviä, sairaita ratsuhevosia.
Jokainen luuli itsensä kutsutuksi näyttelemään suurta sotilasroolia.
Pienempien kaupunkien kahvilat, jotka vilisivät univormuun
pukeuneita kauppiaita, muistuttivat kasarmeja tai sota-
ambulansseja.
Cannevillen kaupunkiin eivät nämä hulluttavat uutiset armeijasta ja
pääkaupungista olleet vielä saapuneet; mutta tavaton kiihotustila oli
vallinnut kaupungissa jo kuukauden päivät ja puolueet seisoivat
ärsytettyinä vastakkain.
Kaupungin pormestari, kreivi de Varnetot, pieni, laiha ja vanha mies,
oli niitä legitimistejä (laillisuuden puolustajia) jotka äskettäin oman
arvonsa vuoksi olivat palanneet keisarikuntaan; päättäväisen
vastustajan oli hän saanut tohtori Massarelista, joka oli suuri,

kuumaverinen mies ja nykyään piirikunnan tasavaltaisen puolueen
johtaja, paikallisen vapaamuurarilooshin presidentti,
maanviljelysseuran ja paikkakuntalaisten klubin esimies sekä uuden,
maalaissotaväen järjestäjä, jonka määrä oli pelastaa valtakunta.
Parissa viikossa oli hänen onnistunut saada 63 vapaaehtoista
aviomiestä ja perheenisää, viisasta talonpoikaa ja kauppapalvelijaa
ryhtymään isänmaan puolustusväkeen; joka aamu harjoitti hän
joukkoansa kaupungin torilla.
Kun pormestari sattumalta tähän aikaan meni torin laidassa olevalle
kunnallishuoneelle, astui komentaja Massarel, revolveri vyöllä ja
miekka kädessä, ylpeänä joukkonsa rintaman ohitse huudattaen
miehistöllänsä eläköön isänmaa! Tämä huuto harmitti
huomattavasti pikku kreiviä, joka epäilemättä näki tässä uhkauksen
ja yllytyksen taisteluun samalla kun se vastenmielisesti muistutti
häntä suuren vallankumouksen ajoista.
Aamulla syyskuun 5 p:nä vastaanotti tohtori univormuun puettuna ja
revolveri pöydällänsä vanhan maalaispariskunnan; aviomies, jota
suonipaisuke oli vaivannut jo seitsemän vuotta, selitti ruvenneensa
pelkäämään, että vaimokin saa saman taudin ja että...
Mutta samassa toi postiljooni pääkaupungin sanomalehden.
Herra Massarel avasi lehden, kalpeni, kääntyi äkkiä poispäin, nosti
innostuksen valtaamana käsivarret ylös taivasta kohti ja huusi täyttä
kurkkua noille maalaishöperöille:
— Eläköön tasavalta! eläköön tasavalta! eläköön tasavalta!
Sitten vaipui hän nojatuoliin ihan voipuneena liikutuksesta.
Ja kun talonpoika yritti jatkaa selitystänsä sanoen: se alkoi sillä
tavoin, nähkääs, että tuntui kuin olisi kusiaisia juoksennellut sääriä
pitkin... huusi tohtori hänelle:
— Jättäkää minut rauhaan! Minulla ei ole aikaa kuunnella teidän
tuhmuuksianne. Tasavalta on julistettu, keisari otettu vangiksi ja
Ranska on pelastettu. Eläköön tasavalta!

Sitten juoksi hän ovelle ja mölähti: Céleste, hoi! Joudu, Céleste!
Säikähtyneenä syöksähti palvelijatar ovelle. Tohtori puhui niin
nopeasti, että se kävi vallan sopotukseksi: — Kenkäni, miekkani,
patruunavyöni ja espanjalainen tikarini, joka on siellä yöpöydälläni:
joudu, joudu!
Taas yritti itsepäinen talonpoika, otollisen äänettömyyden tullen,
jatkaa selitystänsä:
— Siitä syntyi sitten kuin pieniä pusseja, joihin teki kipeää
kävellessä...
Kiivastuneena ärjäsi lääkäri hänelle:
— Jättäkää minut rauhaan, sanon minä! Jos te olisitte pesseet
jalkanne, ei niitä olisi syntynyt, koira vieköön!
Tarttuen sitten miestä kurkusta kiinni tiuskaisi hän tälle vasten
naamaa:
— Etkö sinä, kolmenkertainen elukka, tiedä, että me elämme jo
tasavallassa!
Ammattitunne rauhoitti hänet kuitenkin heti kohta ja hän lykkäsi
ällistyneen pariskunnan ulos toistellen:
— Tulkaa huomenna, tulkaa huomenna, ystäväni. Tänään minulla ei
ole aikaa.
Pukeutuessaan kiireestä kantapäähän jakeli hän jälleen sarjan
tärkeitä käskyjä palvelijattarellensa:
— Juokse luutnantti Picartin ja aliluutnantti Pommelin luo ja sano
heille, että odotan heitä tänne hetipaikalla. Toimita myöskin
Torchebeuf rumpunsa kanssa tänne heti; heti, kuuletko?
Célesten mentyä kokosi hän ajatuksiansa valmistautuen hallitsemaan
uuden asiaintilan vaikeuksia.
Kutsutut saapuivat yht'aikaa tavallisessa työpuvussansa. Päällikkö,
joka odotti heidän tulevan sotilaallisessa asussa, kavahti ylös hieman
kummastuneena.

— Perhana, ettekö te siis tiedä mitään? Keisari on vankina ja
tasavalta on julistettu. Nyt on meidän toimittava. Minun asemani on
arveluttava, sanoisinpa melkein vaarallinen.
Mietittyänsä muutamia sekunteja säikähtyneen alipäällystönsä
edessä, jatkoi hän:
— Meidän täytyy toimia ... ja toimia empimättä. Minuutit ovat tuntien
veroisia tällaisissa tapauksissa. Kaikki riippuu nopeista päätöksistä.
Te, Torchebeuf, lyökää rumpua koko kaupungissa aina Gerisaie'n ja
Salmaren ulkokyliin saakka ja hälyttäkää miehistö aseisiin torille. Te,
Pommel, pukekaa nopeasti univormu yllenne, mutta ainoastaan takki
ja keepi. Me valloitamme maistraatin ja vaadimme kreivi de
Varnetot'in jättämään virkansa meille. Ymmärrättekö?
— Kyllä.
— Mutta toimikaa nopeasti. Minä saatan teitä hra Pommelin luo, sillä
me toimimme yhdessä.
Viisi minuuttia myöhemmin ilmestyivät komentaja-päällikkö ja hänen
aliluutnanttinsa, hampaihin saakka aseestettuina, torille juuri sillä
hetkellä, jolloin pikku kreivi de Varnetot, säärystimet jalassa ja pyssy
olalla — ikäänkuin aikoisi hän metsästämään — marssi nopein
askelin esille eräältä toiselta kadulta, vartioväkenänsä kolme
vihreään takkiin puettua vahtimiestä, lyhyet miekat sivuilla ja kiväärit
riippuen olkahihnoissa.
Sillä välin kuin tohtori ällistyneenä pysähtyi heitä katsomaan,
hävisivät nuo neljä miestä kaupungin taloon, jonka ovi sulkeutui
heidän jälkeensä.
— Hän ehti ennen meitä, murahti tohtori; nyt täytyy meidän odottaa
apuväkeä. Neljännestuntiin emme voi tehdä mitään.
Luutnantti Picart palasi takaisin.
— Pastori kieltäytyi tottelemasta, sanoi hän; kirkonvartijan ja
unilukkarin kanssa sulkeutui hän temppeliin.

Torin toisella puolen, valkoista kaupungintaloa vastapäätä, seisoi
kirkko mykkänä ja mustana, suuri, raudoitettu tammiovi suljettuna.
Kun uteliaat asukkaat, nenä akkunan-ruuduissa, tähystelivät torille
tahi tulivat ulos talojensa kynnyksille katsomaan, oliko jotain tekeillä,
kuului äkkiä rummutus ja pian ilmestyi Torchebeuf torille iskien ihan
riivatusti nuo kolme nopeaa hälytyslyöntiä. Joustavin askelin kulki
hän torin poikki ja katosi sitten erääseen etukaupunkiin vievälle
maantielle.
Päällikkö paljasti miekkansa, astui yksin niiden molempien
rakennusten keskivälille, joihin viholliset olivat sulkeutuneet, huitoi
aseellansa päänsä ylitse ja rönkäsi keuhkojensa koko voimalla:
— Eläköön tasavalta! kuolema pettureille!
Sen tehtyänsä vetäytyi hän upseeriensa luo.
Teurastaja, leipuri ja apteekkari telkesivät akkunaluukkunsa ja
sulkivat myymälänsä. Ainoastaan ruokatavarakauppa jäi avoimeksi.
Tällä välin kokoutui porvarisotilasten miehistö vähitellen torille, mikä
missäkin puvussa, mutta kaikilla kuitenkin punaisella nauhalla
varustettu, musta keepihattu, joka muuten oli heidän ainoa
univormunsa. Kaikki tulivat aseestettuina vanhoilla, ruostuneilla
kivääreillään, jotka jo kolmisenkymmentä vuotta olivat riippuneet
keittiön takka-uunien yläpuolella; tässä asussansa muistuttivat he
kovin elävästi talonvartijain osastoa. Kun noin kolmekymmentä
miestä oli kokoontunut hänen ympärillensä, selitti päällikkö heille
muutamin sanoin, mitä pääkaupungissa oli tapahtunut. Sitten
kääntyi hän taapikuntansa puoleen ja sanoi:
— Ja nyt me toimimme.
Asukkaat kokoontuivat ryhmiin, kyselivät toisiltansa ja puhelivat
rähisten.
Tohtori oli pian tehnyt päätöksensä taistelun suunnitelman suhteen:
— Luutnantti Picart! Te marssitte suoraan kaupungintalon akkunain
eteen ja vaaditte tasavallan nimessä kreivi de Varnetot'in jättämään

talon avaimet minun haltuuni.
Mutta luutnantti Picart, joka oli muurarimestari, kieltäysi tästä
sanoen:
— Te olette aika veitikka, te, hra komentaja. Vai menisin minä sinne
saamaan luodin kylkeeni? Paljo kiitoksia! Tiedättehän, että nuo tuolla
sisässä ampuvat hyvin. Tehkää itse toimituksenne.
Komentaja punastui:
— Järjestyksen nimessä käsken minä teitä sinne.
Mutta luutnantti kieltäysi sittenkin:
— Kuinka antaisin minä runnella itseni tietämättä miksikä?
Kaupungin arvokkaimmat henkilöt, jotka olivat keräytyneet yhteen
ryhmään lähelle sotilastoa, nauroivat. Eräs heistä huudahti:
— Oikein, Picart! Tämä ei ole oikea hetki siihen.
Silloin murahti tohtori:
— Te pelkurit!
Riisuen miekkansa ja revolverinsa, jotka hän jätti eräälle sotilaalle,
lähestyi hän hitain askelin ja silmät akkunoihin luotuina
kaupungintaloa odottaen, että sieltä suunnattaisiin pyssyrämän
piippu häntä kohden.
Tultuansa muutamien askelten päähän näki hän talon molemmissa
päädyissä sijaitsevain koulujen ovien avautuvan ja joukko lapsia,
poikia ja tyttöjä sekaisin, tulvahti sieltä ulos leikkimään suurella,
avonaisella torilla; huitoen käsillänsä kuin olisivat he leikkineet
lintusilla-oloa rähisivät he tohtorin ympärillä niin, ettei tämä saanut
ääntänsä kuuluville.
Kun viimeiset oppilaat olivat ehtineet ulos torille, sulkeutuivat
molemmat ovetkin.
Vihdoinkin hajautuivat poikaviikarit sen verran, että tohtori voi
koroittaa voimakkaan äänensä:

— Hra de Varnetot?
Eräs akkuna ensimäisessä kerroksessa avautui ja hra de Varnetot
ilmestyi akkunaan.
Komentaja jatkoi:
— Arvoisa hra pormestari kai tietää, mitkä suuret tapahtumat juuri
ovat mullistaneet hallituksemme. Se hallitus, jota te täällä edustatte,
ei enää ole olemassa. Olojen näin surullisesti, mutta ratkaisevasti
kääntyessä tulen minä uuden tasavallan nimessä vaatimaan teitä
jättämään minun käsiini sen toimivallan merkit, jotka entinen hallitus
on teille uskonut.
Hra de Varnetot vastasi:
— Hra tohtori, minä olen laillisen hallituksen nimittämä Cannevillen
pormestari, ja aion pysyä tässä toimessani, kunnes minut on
virallisesti siitä vapautettu ja esimiesteni määräyksestä uusi sijalleni
nimitetty. Muuten olen minä isäntä täällä virkatalossa ja aijon jäädä
tänne. Turhaan koetatte minua täältä karkoittaa.
Ja pormestari sulki akkunan.
Komentaja peräysi joukkonsa luo. Mutta ennen kuin hän tälle teki
selkoa käynnistänsä mittasi hän luutnantti Picartia kiireestä
kantapäähän.
— Te olette suupaltti, te, ja pelkuri jänis, joka häpäisette koko
armeijan. Minä erotan teidät virastanne.
— Vähät minä siitä, vastasi luutnantti ja vetäysi murisevaan
väkijoukkoon.
Sitten mietti tohtori, mitä oli tehtävä. Uskaltaako hyökkäys? Mutta
jos sotilaat eivät tottele? Ja oliko hänellä siihen oikeuttakaan?
Eräs ajatus johtui hänen mieleensä. Hän riensi
sähkösanomakonttoriin, joka sattui olemaan aivan maistraattia
vastapäätä, torin toisella puolen.

Täällä kirjoitti hän kolme sähkösanomaa: ensimäisen tasavaltalaisen
hallituksen jäsenille Pariisissa; toisen Ala-Seinen uudelle,
tasavaltalaiselle prefektille Rouenissa ja kolmannen uudelle,
tasavaltalaiselle aliprefektille Dieppessä.
Hän selitti asiain tilan, huomautti vaarasta jättää kunnan asiat
edelleen entisen, monarkkisen pormestarin käsiin, tarjosi uskollista
palvelustansa tasavallalle, pyysi määräyksiä ja kirjoitti nimensä alle
kaikki arvonimityksensä.
Sitten palasi hän joukkonsa luo ja veti 10 frangin kultarahan
taskustaan sanoen: kas tässä, ystäväni, menkää hiukan syömään ja
juomaan; jättäkää tänne vain 10-miehinen osasto pitämään silmällä,
ettei kukaan pääse ulos maistraatista.
Tämän sattui virasta erotettu luutnantti Picart kuulemaan, joka juuri
jutteli kellosepän kanssa; hän alkoi naljailla ja sanoi: Jumalan
tähden, hra komentaja, jos he pääsevät sieltä ulos, niin silloinhan on
teillä tilaisuus päästä sisään. Ell'eivat he sitä tee, niin en ymmärrä,
millä tavoin te sinne pääsette!
Tohtori ei vastannut hänelle mitään, vaan meni syömään aamiaista.
Jälkeen puolen päivän asetti hän sitäpaitse vartijasotilaita ympäri
kaupunkia ikäänkuin hän olisi peljännyt jotakin yllätystä.
Itse kulki hän useita kertoja kaupungintalon ja kirkon ohitse
huomaamatta mitään epäilyttävää; päinvastoin näyttivät nuo
molemmat rakennukset vallan autioilta.
Teurastaja, leipuri ja apteekkari avasivat jälleen myymälänsä.
Kaupungissa liikkui paljo huhuja. Jos keisari oli joutunut vangiksi, oli
se epäilemättä kavalluksen kautta tapahtunut. Varmasti ei tiedetty,
millaiseksi tasavalta oli aiottu.
Ilta alkoi jo hämärtää.
Noin klo 9 illalla lähestyi tohtori yksin ja vallan hiljaa kaupungintalon
ovea varmana siitä, että vastapuolue oli mennyt levolle. Kun hän

yritti murtaa ovea auki pienen rautakangen avulla, kuului äkkiä kova-
äänisen vahtisotilaan kysymys sisäpuolelta:
— Ken siellä?
Silloin peräysi tohtori Massarel vihollisen luota, minkä jalat kantoivat.
Seuraava päivä koitti ilman että mitään oli muuttunut asiaintilassa.
Aseestettu sotaväki hallitsi edelleen toria. Kaupungin väestö oli
keräytynyt sotajoukon ympärille odottamaan ratkaisua; myöskin
naapurikylistä oli kansaa tullut katsomaan, mitä täällä oli tekeillä.
Silloin päätti tohtori, joka jo ymmärsi arvonsa tässä olevan
kysymyksessä, saattaa asiat lopulliseen ratkaisuun tavalla tahi
toisella. Hän mietti juuri, mikä varma ja luja päätös tässä oli tehtävä,
kun sähkölennätinkonttorin ovi avausi ja konttorin juoksutyttö tuli
ulos pari sähkösanomaa kädessä.
Hän riensi heti suoraan komentajaa kohti ja antoi hänelle toisen
sähkösanomista. Sitten juoksi hän allapäin ja pelonalaisena
väkijoukon katseista, jotka kaikki seurasivat hänen liikkeitänsä,
aution torin poikki ja koputti hiljaa kaupungintalon teljetylle ovelle
aivan kuin ei hän olisi tiennytkään, että aseellisia miehiä oli sinne
piiloutunut.
Vartija raotti ovea ja miehen käsi otti sähkösanoman vastaan, jonka
jälkeen tyttönen palasi takaisin aivan punoittavana ja melkein itku
kurkussa siitä, että koko maailma häneen katsoa tuijotti.
Sitten huusi tohtori värähtelevällä äänellä:
— Hiukan hiljaisuutta, hyvät kansalaiset!
Ja kun väkijoukko oli vaiennut, lausui hän ylpeästi:
— Kas tässä tiedonanto, jonka olen uudelta hallitukselta saanut.
Sitten näytti hän sähkösanomaa ja luki:
Entinen pormestari erotettu. Ilmoittakaa se kansalle mitä
pikimmin. Varrotkaa lisäohjeita. Aliprefektin puolesta

Sapin, neuvosmies.
Tohtori riemuitsi. Hänen sydämensä sykki ilosta ja hänen kätensä
vapisivat. Mutta silloin huusi Picart, hänen entinen alaluutnanttinsa,
läheisestä väkijoukosta:
— Tuo kaikki on hyvä. Mutta jos nuo toiset eivät tule ulos tuolta, niin
tuottaa tuo paperi teille vain ... huonot jalat, hra tohtori.
Hra Massarel kalpeni. Jos vastapuolue todellakaan ei aikonut jättää
taloa, niin täytyi hänen nyt marssia eteenpäin. Se ei ollut ainoastaan
hänen oikeutensa, vaan myöskin hänen velvollisuutensa.
Hän katsahti arasti kaupungintalolle päin toivossa, että näkisi oven
avautuvan ja vastustajan peräytyvän.
Mutta ovi pysyi suljettuna. Mitä tehdä? Väkijoukko kasvoi ja kertyi
sotaväen ympärille. Kaikki nauroivat.
Tohtoria harmitti varsinkin eräs ajatus. Jos hän nyt tekisi
hyökkäyksen, täytyisi hänen marssia joukon etunenässä. Ja kun koko
taistelu epäilemättä päättyisi, jos hän kuolisi, niin tähtäisivät hra de
Varnetot ja tämän kolme asemiestä tietysti ainoastaan häntä. Ja ne
olivat hyviä, sangen hyviä ampujia kaikki tyyni. Picart muistutti häntä
siitä vieläkin kerran. Mutta silloin pälkähti eräs ajatus hänen
päähänsä ja hän kääntyi Pommelia kohti sanoen:
— Juoskaa pian apteekkarin luo ja pyytäkää häneltä servietti ja
kävelykeppi.
Luutnantti kiiruhti pois.
Tohtori aikoi tehdä valkean neuvottelulipun, joka ehkä ilahduttaisi
vanhan pormestarin laillista sydäntä.
Pommel palasi jo pyydetyn liinan ja erään luudanvarren kanssa.
Ohkaisella purjenuoralla sidottiin lippu tankoon, johon hra Massarel
kävi molemmin käsin kiinni ja lähestyi nyt uudelleen kaupungintaloa,
pitäen lippua edessänsä.
Päästyänsä ovelle huusi hän:

— Hra de Varnetot!
Ovi aukesi samassa ja hra de Varnetot ilmestyi kynnykselle kolmen
vartijasotilaansa kanssa.
Vaistomaisesti peräytyi tohtori askeleen taapäin. Sitten tervehti hän
vihollista kohteliaasti ja sanoi liikutuksesta tukehtumaisillansa:
— Hra pormestari, minä tulen antamaan teille tiedon saamistani
määräyksistä.
Vastaamatta tohtorin tervehdykseen vastasi vanha ylimys:
— Minä peräydyn, hra komentaja, mutta tietäkää, ett'en tee sitä
pelvosta enkä tottelevaisuudesta minulle vastenmielistä hallitusta
kohtaan, joka nyt on vallan anastanut.
Lausuen nämä sanat sangen harvaan lisäsi hän:
— Minä en tahdo antaa aiheita luuloon, että haluaisin olla
päivääkään tasavallan palveluksessa. Sillä hyvä.
Hämmästynyt tri Massarel ei tiennyt vastata tähän mitään. Hra de
Varnetot lähti nopein askelin tiehensä ja hävisi seurueensa kanssa
torin kulman ta'a.
Ylpeänä palasi tohtori väkijoukon luo. Tultuansa niin lähelle, että hän
otaksui äänensä kuuluvan, huusi hän:
— Hurraa! Hurraa! Tasavalta voittaa kaikkialla!
Mitään liikutusta ei väkijoukossa huomattu.
Tohtori jatkoi:
— Nyt on kansa vapaa, kaikki olette te vapaita ja riippumattomia
kansalaisia. Siitä voitte olla ylpeitä!
Hitaat kyläläiset katsoa töllöttivät häneen, mutta kenenkään silmissä
ei näkynyt mitään kunniantunnon välkettä.
Tohtori tarkasti vuorostaan heitä harmistuneena tuollaisesta
välinpitämättömyydestä ja miettien mitä hänen oli heille sanottava,
mitä hän voisi tehdä vilkastuttaaksensa tätä velttoa kansaa yhdellä

ainoalla iskulla ja miten hän parhaiten voisi täyttää kutsumustansa
kansan herättäjänä.
Silloin valtasi hänet eräs ajatus ja kääntyen Pommeliin päin sanoi
hän:
— Hra luutnantti, käykääpä noutamassa erotetun keisarin rintakuva
kunnallisneuvosten istuntosalista ... ja ottakaa joku tuoli
mukaanne...
Eikä aikaakaan, niin palasi lähetti takaisin kantaen oikealla olallansa
Bonaparten kipsistä rintakuvaa ja vasemmassa kädessänsä tuolia.
Hra Massarel riensi häntä vastaan, otti tuolin, asetti sen maahan ja
nosti valkoisen rintakuvan tuolille; sitten peräytyi hän pari askelta
taapäin ja puhutteli kipsikuvaa sointuvalla äänellä tähän tapaan:
— Tiranni, tiranni, nyt olet sinä kukistunut, kukistunut ja suistunut
maahan ja katulokaan. Kuoleva isänmaa korisi jo saappaasi alla.
Mutta sallimuksen kosto kohtasi sinut. Tappio ja häpeä liittyvät
nimeesi. Sinä kukistut voitettuna ja preussilaisten vankina...
Suistuvan keisarikuntasi raunioille nousee nuori ja säteilevä
tasavalta, joka jälleen kohottaa sinun katkenneen kalpasi...
Tohtori odotti suosionosoituksia. Vaan suosionhuutoja ei kuulunut
eikä kättentaputuksia. Peljästyneet talonpojat pysyivät vaiti ... ja
keisari, jonka ohuiksi punotut viikset ulottuivat ulommaksi poskipäitä
ja joka vaikutti hyvin harjatulta kuin joku vahakuva parturin
akkunassa, näytti katselevan hra Massarelia häviämättömällä ja
ivallisella kipsihymyllänsä.
Näin katselivat he jonkun aikaa toisiansa kasvoihin, Napoleon
tuolillansa ja Massarel seisoalta ... noin kolmen askeleen päässä.
Komentaja Massarel tunsi harmistuvansa. Mitä tehdä? Mitä voi hän
tehdä herättääksensä tämän kansan ja voittaaksensa lopullisesti
yleisen mielipiteen uudelle hallitusmuodolle?
Sattumalta tuli hän laskeneeksi kätensä vatsansa päälle ja tapasi
punaisen vyön alle pistetyn revolverin pään.

Mitään uutta ajatusta, mitään uusia sanoja ei hän enää keksinyt. Sen
sijaan veti hän revolverin vyöstänsä, peräysi pari askelta ollaksensa
sopivan matkan päässä ja ampui entistä hallitsijaansa.
Luoti teki Napoleonin otsaan pienen mustan läven, joka näytti tuskin
huomattavalta täplältä. Laukauksella ei siis ollut mitään vaikutusta.
Silloin ampui tohtori toisen laukauksen, joka teki toisen läven, sitten
kolmannen ja lopuksi vallan peräkkäin kolme viimeistä patruunaa.
Napoleonin otsa pirstausi valkoiseksi tomuksi, mutta hänen silmänsä,
nenänsä ja ohuet, pitkät viiksensä pysyivät koskemattomina.
Epätoivoisena iski tohtori sitten nyrkillänsä tuolin kumoon ja laski
vihdoin jalkansa rintakuvan jäännösten päälle ja kääntyi tässä
voittajan asennossa seisten ällistyneeseen yleisöön päin huudahtaen:
— Noin kukistukoot kaikki kavaltajat!
Mutta kun mitään innostusta ei sittenkään seurannut ja kun
katselijakunta näytti tyhmistyvän hämmästyksestä, huusi komentaja
sotilaillensa:
— Nyt voitte te palata takaisin — kotiinne!
Itse harppasi hän pitkin askelin kotiansa kohti ikäänkuin olisi hän
halunnut paeta tiehensä.
Eteisessä ilmoitti emännöitsijä tohtorille, että eiliset potilaat olivat
varronneet häntä jo neljättä tuntia odotushuoneessa. Tohtori riensi
sinne.
Ja siellä istui tuo suonipaisuketta sairastava pariskunta, joka oli
saapunut jo päivän koittaessa, ja yhä vartoi lääkäriä itsepintaisella
kärsivällisyydellä.
Tämän astuttua sisään, ryhtyi ukko jälleen selittämään:
— Se alkoi, nähkääs, sillä lailla, että tuntui kuin kusiaisia olisi juossut
alinomaa sääriäni pitkin...

SUSI.
Kas tässä kertomus, jonka vanha markiisi d'Arville jutteli meille erään
päivällisen jälkeen parooni Ravelsin luona Saint-Hubertissa.
Sinä päivänä olivat herrat olleet hirvenajossa. Pöytäseurasta oli
markiisi ainoa, joka ei ollut ottanut osaa tähän ajoon, sillä hän ei
ylipäänsä koskaan metsästänyt.
Juhla-atrian kuluessa oli tuskin puhuttukaan mistään muusta kuin
metsänriistan kaadannasta. Myöskin naisia huvittivat nämä julmat ja
usein melkein uskomattomat kertomukset; puhujat ikäänkuin
esittivät heille nämä ihmisten hyökkäykset ja taistelut elukoita
vastaan, huitoivat käsillänsä ja käyttivät kovaa ääntä selittäessään.
Hra d'Arville kertoi hyvin, jopa runollisesti, ja vaikka hän toisinaan
olikin hieman liian korkealentoinen, vaikutti hänen kuvauksensa aina.
Luultavasti oli hän kertonut tämän juttunsa usein ennenkin, koskapa
hän teki sen niin luontevasti ja tarvitsematta etsiä sanoja, jotka hän
aina valitsi taitavasti antaaksensa kuulijoille selvemmän kuvan.
— Hyvät herrat! Minä puolestani en ole koskaan metsästänyt, eikä
sitä tehnyt isäni, ei isoisäni eikä myöskään iso-isäni isä. Viime
mainitun isä taas oli mies, joka eläessänsä metsästi enemmän kuin
te kaikki yhteensä. Hän kuoli v. 1764. Tahdonpa kertoa teille, kuinka
tämä tapahtui.
Hänen nimensä oli Jean, naimisissa oli hän myös ja, kuten sanottu,
oli hänellä jo poika. Hän asui yhdessä nuoremman veljensä François
d'Arvillen kanssa sukulinnassamme Lotringissa, suurten metsien
keskellä.
François d'Arville oli pysynyt naimatonna metsästysintonsa vuoksi.
Molemmat metsästivät he vuodet alusta loppuun, levähtämättä,
lakkaamatta ja väsymättä. He eivät rakastaneet muuta, eivät

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