Semantic Knowledge Representation for Information Retrieval Winfried Gödert
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- 5. Semantic Knowledge Representation for Information Retrieval Winfried Gödert Digital Instant Download Author(s): Winfried Gödert, Jessica Hubrich, Matthias Nagelschmidt ISBN(s): 9783110329704, 3110329700 Edition: Digital original File Details: PDF, 7.75 MB Year: 2014 Language: english
- 7. Winfried Gödert, Jessica Hubrich, Matthias Nagelschmidt Semantic Knowledge Representation for Information Retrieval
- 9. Winfried Gödert, Jessica Hubrich, Matthias Nagelschmidt Semantic Knowledge Representation for Information Retrieval
- 10. This work has been published with the financial support of the Cologne University of Applied Sciences. ISBN 978-3-11-030477-0 e-ISBN 978-3-11-032970-4 Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet http://dnb.dnb.de. © 2014 Walter de Gruyter GmbH, Berlin/Boston Typesetting: Michael Peschke, Berlin Cover image: bentaboe/iStock/Thinkstock Printing: Hubert & Co. GmbH & Co. KG, Göttingen ♾ Printed on acid-free paper Printed in Germany www.degruyter.com
- 11. Preface An information seeker – in our context usually referred to as user or end user of search interfaces of collections of information resources like online libraries, domain-specific databases, or the World Wide Web – thinks of something he or she wants to find in a collection. “Something” may be of a very specific or of a very vague kind. Search operations are always designed with the intention to rec- oncile as far as possible these individual conceptualizations of a person’s search interests with the represented conceptualizations of stored indexing data. The retrieval success highly depends on a suitable correspondence between these two components. Information seekers commonly express their search interests in words that they think are grasping best the intended meaning and thus promise best-possible retrieval results. The used words either comply with semantically controlled terms of an indexing language or constitute free-text tokens. They reflect conceptual ideas whose meaning does not manifest itself in isolated con- cepts as it includes a time-dependent context and semantic relations to other concepts. Although recent trends explore semantic relations with statistical and linguistic methods, there are reasons for cognitively analyzing the context as well, to represent it adequately and thereby to provide additional support for automated processes. This is particularly true for information systems that are designed to facilitate knowledge exploration and searching in semantic context by inference or reasoning processes. Historically, there are at least two essential approaches for representing semantic connections between entities of artificial languages: on the one hand indexing languages that are used for representing the content of information resources, on the other hand knowledge representation systems that are used for machine-based knowledge exploration. Combining both approaches might sig- nificantly improve the efficiency of subject-oriented search processes. Within the framework of document indexing, extensive methods have been developed for representing concepts as elements of controlled indexing languages and using them as tools for retrieval processes. Indexing languages represent common knowledge – or more precisely, extracts of common or specialist knowl- edge – in a standardized manner and provide terminological building blocks for subject indexing. As connectors between specific knowledge and corresponding information spaces, they significantly improve thematic access to documents described in form of bibliographic data in a way other systems cannot cope with. Modeled conceptual structures reflect familiar knowledge contexts that are pri- marily processed for cognitive interpretation. They point to connections informa- tion seekers are possibly not aware of but that might nevertheless have a positive impact on the success of the search process if proposed to them. Frequently, the
- 12. vi Preface resources of interest are indexed by headings that are not the first wordings the seeker thinks of, and only by offering such headings as additional vocabulary to the seeker positive retrieval results are obtained. Traditionally, such relationships are not regarded as tools for machine-supported analysis. Therefore, they are not sufficiently formalized for automatic reasoning processes. Usually, attributes or properties justifying a particular relation between two concepts are not stated explicitly. Relational structures commonly make use of a rather small set of rela- tionships that is not expressive and does not allow making precise, differentiated statements about semantic connections. Until now, mainly theoretical proposals give valuable hints for creating an adequate inventory of specified relation types; there are only very few attempts for practical realization. In the context of artificial intelligence, systems for knowledge representa- tion have been developed that focus on formal considerations and techniques for modeling knowledge, neglecting issues of indexing and retrieval of documents. They primarily aim at enabling machine processing and especially at drawing inferences on the formalized knowledge level. Expert or diagnostic systems give respective examples. Document indexing and retrieval are considered in the context of special applications, if at all. In general, existing indexing languages are not included; tools for knowledge representation are rather newly created or recreated. The conception of a Semantic Web marks a new step of development. It is proposed that distributed data resources should be technically combined, and it is envisioned that appropriate ontological representing and linking of distrib- uted resources could generate an additional semantic value from which thematic search processes could enormously benefit. As a matter of fact, some retrieval tests could already adduce the empirical evidence that ontology-based search processes lead to a higher performance than keyword-based searches. However, it is not clear yet how subject indexing and document retrieval can benefit from these visionary and technological impulses and how appropriate strategies for realization could look like. These questions are far from being trivial. This is reflected by the fact that the focus has shifted from Semantic Web to Linked Data applications. These intend to achieve added semantic value by merely connect- ing existing data reservoirs, making them technically interoperable. Combining cognitive and mechanical interpretation of semantic data for improving retrieval efficiency and retrieval results lies outside the interest of such projects. Yet, a semantic space that is cognitively and at the same time machine-interpretable and that brings together different existing and newly created resources for the benefit of knowledge acquisition and information retrieval is the most challeng- ing idea connected with the Semantic Web. In such a space, information seekers
- 13. Preface vii could formulate their cognitive interests and automated tools would subsequently provide additional support that would lead to an improved search success. When designing improved search environments, it is important to ensure that content-descriptive terms of different systems are exchangeable, that seman- tic entities are interoperable. Suitable models of semantic interoperability would support both, switching between different indexing languages as well as combin- ing entities of more than one indexing language to execute thematic queries. In case valid conclusions on the conceptual level were reached, it would be essen- tial considering not only mechanical interoperability and string matching but also the semantic content of entities and the relational structure of the respective indexing languages. The final stage may be characterized as ontology-based indexing and retrieval with respect to semantic interoperability in heterogeneous environ- ments. Combining the methodological approaches to the semantic representa- tion standards of the Semantic Web provides the opportunity to separate from proprietary application contexts. Already developed knowledge structures can be used for or shared with other applications in the sense of a content-oriented semantic interoperability. The main character of this book can be described as twofold. First, it gives a state-of-the-art report with regard to the mentioned issues. It presents a frame- work for interconnecting the described two strands of development and shows how they can benefit from each other. In particular, it is discussed how document retrieval and search results can be improved based on an expanded set of differ- entiated semantic relation types that allow for drawing machine inferences along the relational structure. Secondly, it contains proposals to which extent existing indexing languages can be used and what requirements have to be met to develop them further towards knowledge representations being able to fulfill both the conceptual interpretations of their elements and to support formal inferences for the design of advanced retrieval environments. This part of the book is based on two projects that were conducted at the Cologne University of Applied Sciences during the years 2006 to 2011: CrissCross and Reseda. The CrissCross project was financially supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) and was executed in cooperation with the German National Library. It aimed at creating a multilingual, thesaurus-based and user-friendly research vocabulary that facilitates research in heterogeneously indexed collections. To achieve this aim the subject headings of the German subject headings authority file Schlagwortnormdatei (SWD) were mapped to notations of the Dewey Decimal Classification, i.e., its German version (DDC Deutsch). Within its framework, the German National Library also linked SWD headings to their equivalents in the Library of Congress Subject Headings
- 14. viii Preface (LCSH) and the French indexing vocabulary Rameau, thus contributing to the MACS project. The results of the project became part of the Linked Data service of the German National Library. The experiences and expertise gained in the CrissCross project were utilized within the second project, Reseda - Representational models for semantic data. This project was made possible by the financial support of the Cologne Univer- sity of Applied Sciences. Its focus was on designing, developing and improving models and frameworks for the representation of semantic information in knowl- edge organization systems. The project’s aim was to explore strategies for pre- cisely specifying the semantic content and characteristics of concepts and the semantic relations between these concepts in indexing languages and other knowledge organization systems, thereby augmenting the semantic richness and expressivity of these vocabularies for machine support within retrieval scenarios. Many results of this project form the basis of this book. Initial and target point of all considerations presented in this book are pro- cesses of information retrieval for subject content, viz. automatic and cognitive strategies to explore knowledge or to facilitate access to information. An introductory chapter gives a description of the problems and objectives for solutions, technical details of the subsequent discussion are thus not antici- pated. From the perspective of the authors, the selected sample environment has a special aptitude for this objective. For the subsequent discussion, however, it is not of substantive importance. The focus of the considerations are always general problems and solutions. All of the examples of the book are designed to support abstract considerations or to illustrate general methods. None of the displayed methods is designed for a specific example of the sample environment alone. After this introduction, the text is divided in three parts, each describing a stage for the development of a concept that we call an “ontology-based model for indexing and retrieval”. The first part reports state-of-the-art essentials of knowl- edge organization, indexing principles, and paradigms of information retrieval. Essential characteristics of semantic technologies for knowledge representation are introduced in Chapter 3. The basic features of web-specific representation languages for semantic content are sketched as far as they are of special interest for our context. Besides XML, RDF, and OWL, application-specific representation languages are described. Chapter 4 discusses different levels of semantic expres- sivity in search processes and how the resulting requirements can be supported by combining features of indexing results and retrieval environments. Limita- tions indexing languages face in view of multilingual and heterogeneous infor- mation spaces are also outlined. Part B presents in its first chapter various approaches for handling hetero- geneity in indexing and retrieval, including citation pearl growing, multilingual
- 15. Preface ix indexing languages, and vocabulary linking. Design and outcomes of several projects are presented. It is questioned whether these approaches can be seen as possible solutions for a heterogeneity treatment that human beings can interpret and that at the same time are promoting machine supported inferences. The latter aspect gives rise for continuing the discussion in Chapter 6 by a more detailed analysis of the problems that must be taken into concern if heterogeneity should be solved be methods of semantic interoperability. It is clarified how semantic interoperability should be understood for indexing and retrieval purposes and how to combine this understanding with a model for conceptual knowledge representation by entities and improved relational structures. Conditions under which entities of different indexing languages can be viewed as semantically interoperable are derived as requirements for the following discussion. The third part presents in 4 chapters the components of our understanding of a model for ontology-based indexing and retrieval by combining the established methods of indexing and retrieval with the strength of formal knowledge repre- sentation. In more detail, the primarily cognitively interpretable terms and the established relations between them are embedded into a formal framework of semantic models, typed relations and inference procedures to develop enhanced procedures of search and find scenarios. Within this frame, refining and restruc- turing their relational inventories is indispensible. Based on first examples, we show the potential of specified, logically valid semantic data being interpretable both for cognitive and machine-supported information retrieval processes. We devote special attention to the crucial task of enriching and restructuring existing indexing languages viz. refining the relational inventory by means of abstraction and generalization. The presentation concludes with a short discussion of some open questions and suggestions for further research. Although the chapters are based on each other in content, it was the aim to make each chapter as self-explanatory as possible. In doing so, duplication and cross-references could not always be avoided. Sometimes the re-treatment of a question under a changed point of view was required. The chosen cross-disci- plinary approach made it necessary in some places to use an own terminology. The particularly important terminological definitions have been compiled in a systematic glossary in the appendix. Many colleagues have substantially supported our work and contributed to our findings especially by patient and continuous discussions. At first, we would like to mention the members of the Cologne staff of both projects CrissCross and Reseda: Anne Betz, Felix Boteram, Jan-Helge Jacobs, Tina Mengel, Katrin Müller and Michael Panzer (neé Preuss). We would like to thank them all; our work would not have been successful without their help. A special thanks to Jens Wille
- 16. x Preface who set up a Web search environment for our experiments with typed relations and thus allows performing the first tests as well as verifying our statements. We also got benefit from many persons we cannot mention all by name, especially the members of our project partner institutions and other colleagues interested in our work. We wish to thank them, too. Winfried Gödert Jessica Hubrich Matthias Nagelschmidt
- 17. Table of Contents Preface v 1 Introduction: Envisioning Semantic Information Spaces 1 Part A Propaedeutics – Organizing, Representing, and Exploring Knowledge 2 Indexing and Knowledge Organization 15 2.1 Knowledge Organization Systems as Indexing Languages 15 2.1.1 Building Elements: Entities and Terms 16 2.1.2 Structural Elements: Intrasystem Relations 21 2.1.3 Result Elements: Indexates 27 2.2 Standards and Frameworks 30 2.2.1 ISO 25964: Thesauri and Interoperability with other Vocabularies 30 2.2.2 Functional Requirements for Subject Authority Data (FRSAD) 31 3 Semantic Technologies for Knowledge Representation 33 3.1 Web-based Representation Languages 33 3.1.1 XML 34 3.1.2 RDF/RDFS 37 3.1.3 OWL 42 3.2 Application-based Representation Languages 49 3.2.1 XTM 50 3.2.2 SKOS 57 4 Information Retrieval and Knowledge Exploration 61 4.1 Information Retrieval Essentials 61 4.1.1 Exact Match Paradigm 62 4.1.2 Partial Match Paradigm 64 4.2 Measuring Effectiveness in Information Retrieval 65 4.3 From Retrieving to Exploring 68 4.3.1 String-based Retrieval Processes 71 4.3.2 Conceptual Retrieval Process 73 4.3.3 Conceptual Exploration Processes 74 4.3.4 Topical Exploration Processes 78 4.4 From Homogeneous to Heterogeneous Information Spaces 80
- 18. xii Table of Contents Part B Status quo – Handling Heterogeneity in Indexing and Retrieval 5 Approaches to Handle Heterogeneity 87 5.1 Citation Pearl Growing 87 5.2 Modeling Multilingual Indexing Languages 89 5.3 Establishing Semantic Interoperability between Indexing Languages 90 5.3.1 Structural Models 91 5.3.2 Mapping Levels 93 5.3.3 Vocabulary Linking Projects 96 6 Problems with Establishing Semantic Interoperability 105 6.1 Conceptual Interoperability between Entities of Indexing Languages 107 6.1.1 Focused and Comprehensive Mapping 108 6.1.2 Conceptual Identity and Semantic Congruence 112 6.2 Equivalent Intersystem Relationships 118 6.2.1 Intersystem Relations Compared to Intrasystem Relations 119 6.2.2 Interoperability and Search Tactics 121 6.2.3 Specified Intersystem Relationships 132 6.2.4 Conceptual Interoperability between Indexing Results 134 6.2.5 Directedness of Intersystem Relationships 137 Part C Vision – Ontology-based Indexing and Retrieval 7 Formalization in Indexing Languages 147 7.1 Introduction and Objectives 147 7.2 Common Characteristics and Differences between Indexing Languages and Formal Knowledge Representation 151 7.3 Prerequisites for an Ontology-based Indexing 156 7.3.1 Semantic Relations and Inferred Document Sets 158 7.3.2 Facets and Inferences 167 8 Typification of Semantic Relations 181 8.1 Inventories of Typed relations 182 8.2 Typed Relations and their Benefit for Indexing and Retrieval 188 8.3 Examples of the Benefit of Typed Relations for the Retrieval Process 194
- 19. Table of Contents xiii 8.3.1 Example 1: Aspect-oriented Specification of the Generic Hierarchy Relation 194 8.3.2 Example 2: Typed Relations of a Topic Map built from the ASIST Thesaurus 197 8.3.3 Example 3: Degrees of Determinacy 213 9 Inferences in Retrieval Processes 215 9.1 Inferences of Level 1 216 9.1.1 Hierarchical Relationships 216 9.1.2 Associative Relationships 217 9.1.3 Typification of the Synonymy / Equivalence Relationship 218 9.2 Inferences of Level 2 and of Higher Levels, Transitivity 222 9.2.1 Hierarchical Relationships 223 9.2.2 Unspecific Associative Relationships 226 9.2.3 Typification of Associative Relationships 229 9.3 Inferences by Combining Different Types of Relationships 231 9.3.1 Synonymy Relation with Hierarchical Relationships 231 9.3.2 Chronological Relation with Hierarchical Relationships 232 9.3.3 Transitions from Associative Relationships to a Hierarchical Structure 232 9.3.4 Transitions from a Hierarchical Structure to Associative Relationships 233 9.3.5 Transitivity for Combinations of Typed Associative Relationships 235 10 Semantic Interoperability and Inferences 237 10.1 Conditions for Entity-based Interoperability 237 10.2 Models of Semantic Interoperability 244 10.2.1 Ontological Spine and Satellite Ontologies 244 10.2.2 Degrees of Determinacy and Interoperability 250 10.2.3 Entity-based Interoperability and Facets 252 10.3 Perspective: Ontology-based Indexing and Retrieval 254 11 Remaining Research Questions 259 11.1 Questions of Modeling 259 11.2 Questions of Procedure 260 11.3 Questions of Technology and Implementation 262
- 20. xiv Table of Contents Part D Appendices Systematic Glossary 265 Abbreviations 271 List of figures 273 List of tables 277 References 279 Index 289
- 21. 1 Introduction: Envisioning Semantic Information Spaces Indexing languages, interoperability, information retrieval, semantic technolo- gies – is it really worth examining the particular interaction of these rather dif- fering subjects, as we do in this book? In this preliminary chapter we try to give a first answer why we think it is. Therefore we will pick up the idea of a semantic information space again, which was already mentioned in the preface and make it more concrete by envisioning some examples. We will take a first naive look at search situations and the impact of semantic knowledge representation, yet without considering the conceptual or technical background. Thus in this first look, information retrieval systems, indexing languages and semantic technolo- gies are treated as a black box, which ideally provides a search environment that can be somehow characterized as a semantic information space. Examples in this book are heterogeneous and (amongst some others) taken from the domains of chemistry, physics and biology, particularly ornithology. Although neither the authors nor the subjects of this book are affiliated to these disciplines, we will nevertheless occasionally revert to them, as they are clearly outside of our own profession and can be seen insofar as a “neutral” domain, which seems to provide a lower risk of misunderstanding than examples from the less accurate fields of humanities or social sciences would probably provide. However, there are of course no special skills in natural sciences needed to read and understand the examples and to follow the argumentation. All examples are trivial enough to be understood even without any substantial chemical, physical or zoological knowledge. When speaking of an “information space”, one could quite generally think of two extremes: either a collection of information resources that are widely homog- enous in form and content and centralized in one storage or a heterogeneous col- lection, distributed over several repositories and organized independently from each other – the first extreme is e.g. embodied by traditional library collections, while the most prominent example for the latter is the World Wide Web. In the following, both extremes and every possible specification between them shall be understood as information spaces. We begin our consideration with a relatively simple organized information space. Figure 1.1 shows a situation that is remindful of a bibliographic database. The document store contains a number of bibliographic records, which are repre- senting two monographs written by the German chemist and Nobel Prize laureate Otto Hahn and one book of correspondence from the physicist Lise Meitner to Otto Hahn. To represent the authorship of Otto Hahn and Lise Meitner for each docu-
- 22. 2 1 Introduction: Envisioning Semantic Information Spaces ment consistently, a name authority file is used, which contains personal name authority records of both scientists that can be linked to the stored documents. In doing so, one can easily search the information space e.g. for all documents written by Otto Hahn – this search operation is often referred to as a collocation search. Fig. 1.1: Authority files in information spaces. Another search operation can be described as a subject search. That would be a search e.g. for all documents about “radioactivity”. To carry out subject searches, the information space must somehow provide the information of what each doc- ument is “about” – in the indexing context we also speak of the aboutness of a document (cf. Ingwersen 1992, 50–54). In bibliographic databases this aboutness is traditionally represented by one or more subject headings or thesaurus descrip- tors. In order to provide a consistent representation, the subject headings can be organized in a subject headings authority file, so that each subject heading has its own authority record that can be linked to the appropriate document records (cf. Fig. 1.1). There is nothing special to the situation described so far and everybody who has ever used an online catalog of a library should be familiar with it, as it corre- sponds to the way bibliographic data has been organized for a long time and still
- 23. 1 Introduction: Envisioning Semantic Information Spaces 3 continues to be organized by documentary institutions and especially libraries. However, knowledge representation is beginning beyond this situation. In Figure 1.2 the authority files are replaced by a network-like structure. The now grey shaded elements of Figure 1.1 seem to become more complex, as they are somehow embedded in a meaningful context – later on in this book, we will address these elements precisely and speak more abstractly of entities of a knowl- edge representation. What we are characterizing here rather vague as a “meaning- ful context” raises these entities from the keyword-based level in Figure 1.1 to a conceptual level in Figure 1.2. We will examine this important step in the follow- ing chapters and confine ourselves here to the determination that these concepts primarily can be used for indexing the stored documents and thereby fulfill the same basic descriptor function as simple keywords, but that they also open up a broader context, as they are connected to other, somehow related concepts. In the following, this situation will be referred to as a knowledge structure. Fig. 1.2: Knowledge structures in information spaces. Searching the information space in Figure 1.2 with a descriptor “radioactivity” leads not only to the indexed monograph of Otto Hahn “Applied radiochemistry”, but also to the related descriptors “activity level” and “radioisotope”. It becomes apparent that an information seeker, who is interested in “radioactivity”, could also be interested in certain levels of radioactivity or in concrete radioactive iso-
- 24. 4 1 Introduction: Envisioning Semantic Information Spaces topes. The same seems to apply to “nuclear fission” and “nuclear reaction” – it isn’t unlikely that an information seeker with an interest in nuclear fission may also be interested in other nuclear reactions. Beyond that, the knowledge struc- ture of Figure 1.2 also establishes a relationship between Otto Hahn and the rather abstract concept “person” explicit, as well as between Otto Hahn’s research col- league Lise Meitner and “person”. As a human there’s no difficulty in the cogni- tive interpretation of these relations – we can easily see that Otto Hahn and Lise Meitner are persons, even if we never heard their names before. By using seman- tic technologies, this knowledge can be made machine-readable, so that it would be able to infer (Glossary C3.2) that Otto Hahn is a person due to the fact that the concept “Hahn, Otto” is related to the concept “person” in a specific way. Like- wise the risk of confusing the person Otto Hahn with the homonymous research vessel, which was launched in 1964 and named after the famous scientist, could be avoided. At this point we have already mentioned many aspects and reached to the core issues of this book. In the following, we will take a closer look at searches in information spaces and the underlying information retrieval processes and there- fore give a first impression of the usefulness of relations like the above described. We will also look at the interdependency between indexing and information retrieval processes, introduce Knowledge Organization Systems (KOSs) as types of knowledge structures that are designed to support indexing and retrieval and finally concern questions like how it could be made explicit and recognizable for a KOS that a document “Letters of Lise Meitner to Otto Hahn” is about letters that Lise Meitner wrote to Otto Hahn and not vice versa. Based on this, we will provide a more systematic discussion of the specific types of relations and their functionality within and between knowledge struc- tures – later on we will speak of them as intra- and intersystem relations. Yet, before that, some preliminary considerations will be provided, in order to facili- tate a better understanding of the mentioned issues. Accordingly, we will address the functionality of intersystem relations, i.e., those relations that are bridging two knowledge structures and therefore make them somehow interoperable. In this context, we will focus on the problems of heterogeneity that may arise e.g. from the use of different knowledge structures for indexing purposes. This is denoted in Figure 1.3, where single concepts of our introduced example knowledge structure are linked to other, really existing struc- tures, namely the Library of Congress Subject Headings (LCSH), the International Nuclear Information System / Energy Technology Data Exchange (INIS/ETDE), and the YAGO project.
- 25. 1 Introduction: Envisioning Semantic Information Spaces 5 Fig. 1.3: Interoperability in information spaces. These three structures, which were arbitrary selected for this example, are quite different in their organization, coverage and purpose. The LCSH can be charac- terized as an authority file, INIS/ETDE is a thesaurus that has been developed and used by the International Atomic Energy Agency (IAEA)1, and YAGO is an ontology mainly built up with vocabulary from the Wikipedia2. Since we haven’t 1 http://www.iaea.org/inis/products-services/thesaurus 2 http://www.mpi-inf.mpg.de/yago-naga/yago
- 26. Discovering Diverse Content Through Random Scribd Documents
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