Strong semantics : Ontolgy

• Introduction Subhashis Das

• Ontology language Sonali Kishore Kalani

• Ontology Engineering Tools Amit Kumar Shaw

• Application of Ontologies Mayukh Biswas

• Conclusion Anurodh Kumar Sinha

Markup consists of:
rendering information
(e.g., font size and colour)
Hyper-links to related
content
Semantic content is accessible
to humans but not (easily) to
computers…

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• External agreement on meaning of annotations
– E.g., Dublin Core
• Agree on the meaning of a set of annotation tags
– Problems with this approach
• Inflexible
• Limited number of things can be expressed

• Use Ontologies to specify meaning of annotations
– Ontologies provide a vocabulary of terms
– New terms can be formed by combining existing ones
– Meaning (semantics) of such terms is formally specified
– Can also specify relationships between terms in multiple
ontologies

Ontology
Relationships, constraint
s, rules

Strong Semantics
Thesaurus
Equivalence, homographic, hiera
rchical and associative
relationships

Taxonomy
Structure, hier
archy, parent-
child
relationships

Controlled vocabulary

Weak Semantics

-----By Thomas Robert (Tom) Gruber (1994)

‘A formal, explicit specification of a shared conceptualization’

must be machine
not private to some individual,
understandable
but accepted by a group

types of concepts and an abstract model of some
constraints must be clearly phenomenon in the world formed by
defined
identifying the relevant concepts of
that phenomenon

v To share common understanding of the structure of information among people or
software agents.

v To enable reuse of domain knowledge.

v To make domain assumptions explicit.

v To separate domain knowledge from the operational knowledge.

v To analyze domain knowledge.

r Defining terms in the domain and relations among them
v Identifying the domain.
v Defining concepts in the domain (classes). (human, animal, food, table, movies, etc..)
v Arranging the concepts in a hierarchy (superclass -Subclass hierarchy). (Ex-
Animal
-herbivorous
-omnivorous
-carnivorous)
v Defining which attributes and properties classes can have and constraints on their
values.
Attributes (data properties), i.e. human has properties of
gender, height, weight, father, mother, etc.
Properties (Relations), i.e. Indian Statistical Institute is located in Bangalore.
HERBIVORES= only eat vegetables for example elephants are herbivores
CARNIVORES= only eat meat for example tigers are carnivores
OMNIVORES= omnivores eat both meat and plants for example dogs are omnivores

v Defining individuals and filling in properties values.

The three major uses of Ontologies are:
v To assist in communication between humans and computer.

v To achieve interoperability and communication among software systems.

v To improve the design and quality design and the quality of software system.

The term ‘procedure’ used by one tool
is translated into the term ‘method ‘
used by the other via the ontology,
whose term for the same underlying
procedure concept is ‘process’.
give me the procedure for…
viewer

here is the give me the
procedure for… translator procedure = ??? process for…

procedure =
process Ontology ??? = process

give me the
METHOD = translator METHOD
process for…
here is here is the
the process for… METHOD for… method
library

A piece of furniture consisting of a
seat, legs, back, and often
arms, designed to accommodate one
person.

Chair

Something I can sit

?????

Chair Seat Stool Bench

Something I can sit

“Sittable”


Something I can sit on

“Sittable”
Table


“Sittable”

“For_sitting” Table


material

is_a
room “Sittable”
is_a Wood

classroom is_a is_a
Dining room
is_a
is_a is_a is_a


material
made_of
is_a
room “Sittable”
is_a Wood
made_of

classroom is_a
is_a
Dining room
is_a
is_a is_a is_a


• Ontologies generally describe:
v Classes
sets, collections, or types of objects (Ex-Person, animal, food, table, etc.)
v Individuals
the basic or “ground level” objects (Ex- Subhashis Das is an Individual of Class
Person)
v Relationships
ways that objects can be related to one another (Subhashis Das lives in Kolkata )
v Attributes
properties, features, characteristics, or parameters that objects can have and
share
( Subhashis Das has properties of gender, height, weight, hair colour, mobile no, etc)

From a practical view, ontology is the representation of something we know about.
“Ontologies" consist of a representation of things, that are detectable or directly
observable, and the relationships between those things.

• Sir Ratan Naval Tata (born 28 December 1937) is an Indian businessman who
became chairman (1991– ) of the Tata Group, a Mumbai-based conglomerate. He
is a member of a prominent family of Indian industrialists and philanthropists (Tata
family). Tata received the Padma Bhushan, one of India’s most distinguished
civilian awards, in 2000 and Padma Vibhushan in 2008. He has also been ranked as
India's most powerful CEO. Ratan Tata was adopted to famous Tata , a prominent
family belonging to the Parsi community. Ratan is the grandson of Tata group
founder Jamsedji Tata. (http://en.wikipedia.org/wiki/Ratan_Tata)

Relations: is-a, received, is-CEO-of, is_granson_of,
Ratan Tata has Properties of
Gender: male
DOB: 28 Dec, 1937
Race: Parsi
Administrative role: CEO of Tata group

Requirements for an ontology language

• A well defined syntax

• A well-defined semantics

• Efficient reasoning support

• Adequate expressive power

• Convenience of expression

RDF/RDF Schema
• Used for describing resources on web
• Written in XML
• W3C recommendation
• RDF Schema is an extension of RDF
• Provides the framework to describe application-
specific classes and properties instead of actual
application classes and properties
• Similar to classes in OOP languages

Basic Building Blocks of RDF Schema

• Classes and their instances

• Binary properties between classes

• Organization of classes and properties in
hierarchies

• Domain and range restrictions

Limitations of RDF Schema

• Local Scope of properties

• Disjointness of classes

• Boolean combinations of classes

• Cardinality restrictions

• Special characteristics of properties

OWL, a Web Ontology Language
• OWL stands for Web Ontology Language
• OWL is for processing information on the web
• Three sublanguages
– OWL Full
– OWL DL
– OWL Lite
• Build on top of
– XML
– RDFS
• Similar to RDF but with much stronger syntax and larger
vocabulary
• OWL is a W3C standard

OWL Full
• Maximum expressiveness
• Fully upward compatible with RDF
• OWL Full allows an ontology to enhance the
meaning of the pre-defined (RDF or OWL)
vocabulary
• All language constructors can be used in any
combination as long as it is legal RDF
• Reasoning software are not able to support every
feature of OWL Full

OWL DL
• Based on Description Logic
• Maximum expressiveness without losing
completeness
• Widely available reasoning systems
• Constraints:
– Vocabulary partitioning
– Explicit typing
– Property separation
– No transitive cardinality restrictions
– Restricted anonymous classes

OWL Lite
• Must be an OWL DL ontology
• The constructors
owl:oneOf, owl:disjointWith, owl:union
Of, owl:complementOF and owl:hasValue
are not allowed
• Cardinality statements can be made only on values 0
or 1.
• owl:equivalentClass cannot be made between
anonymous classes, but only between class
identifiers

RDF OWL OWL OWL
XML RDF
Schema Lite DL Full

Increasing Semantic Expressiveness

Building Blocks in OWL…[contd.]
• Ontology declaration (XML syntax)
<rdf:RDF xmlns:owl =http://www.w3.org/2002/07/owl#"
xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:xsd ="http://www.w3.org/2001/XMLSchema#">

• Ontology metadata (information about the ontology)
<owl:Ontology rdf:about="">
<rdfs:comment>An example OWL ontology</rdfs:comment>
<owl:priorVersion
rdf:resource="http://www.mydomain.org/uni-ns-old"/>
<owl:imports
rdf:resource="http://www.mydomain.org/persons"/>
<rdfs:label>University Ontology</rdfs:label>
</owl:Ontology>

Building Blocks in OWL
• Classes
– Every class is a descendant of owl:Thing
– Classes are defined using owl:Class
– Equivalence is defined using owl:equivalentClass
• Subsumption
– Provided by owl:subClassOf
• Partitions
– Disjoint partition owl:disjointWith
– Exhaustive partition owl:oneOf

• Attributes (properties)
– Datatype properties: Allows to describe a specific
aspect of a concept
• Based on XSD data types
• The range specifies the data type
• The domain specifies the class to which the property is
referred
– E.g.: Phone, title, age
– Object properties: Attributes that define
relationships between classes (Relations)
• E.g.: isTaughtBy(Class(course), Class(professor))

• Relationships
– Directed
• From one concept to another, no vice versa
– Defined through object properties
• Domain: the class(es) from which the relation departs
• Range: the relation destination(s)
– Subsumption between relationships is possible

• Instances (Individuals)
– No unique name assumption in OWL
– If two instances have a different name or ID this does
not imply that they are different individuals
• E.g.: “Queen Elizabeth”, “The Queen” and “Elizabeth
Windsor” might all refer to the same individual
– It must be explicitly stated that individuals are the
same as each other, or different to each other
– Defined by means of rdf:Description + rdf:Type

• Advanced constructs
– OWL supports several advanced constructs to define
classes and relationships
– Constraints defined on attribute values (either object
or datatype properties)

Special Properties

• owl:TransitiveProperty

• owl:SymmetricProperty

• owl:FunctionalProperty

• owl:InverseFunctionalProperty

OWL Class Constructors

Constructor DL Syntax Example Modal Syntax

intersectionOf C1 Π … Π Cn Human Π Male C1 Λ…Λ Cn
unionOf C1 ∐ … ∐ Cn Doctor ∐ Lawyer C1 ∨ … ∨ Cn
complementOf ¬C ¬Male ¬C
oneOf {x1} ∐ … ∐ {xn} {John} ∐ {Mary} x1 ∨ … ∨ xn
allValuesFrom ∀P.C ∀hasChild.Doctor [P]C
someValuesFrom ƎP.C ƎhasChild.Lawyer <P>C
maxCardinality ≤nP ≤1hasChild [P]n+1
minCardinality ≥nP ≥2hasChild <P>n

OWL Axioms
Axiom DL Syntax Example
subClassOf C1 ⊑ C2 Human ⊑ Animal Π Biped
equivalentClass C1 ≡ C2 Man ≡ Human Π Male
disjointWith C1 ⊑ ≦ C2 Male ⊑ ¬ Female
SameIndividualAs {x1} ≡ {x2} {President Bush} ≡ {G W Bush}
DifferentFrom {x1} ⊑ ≦ {x2} {John} ⊑ ≦ {Peter}
subPropertyOf P1 ⊑ P2 hasDaughter ⊑ hasChild
equivalentProperty P1 ≡ P2 Cost ≡ Price
inverseOf P1 ≡ P2⎺ hasChild ≡ hasParent⎺
transitiveProperty P+ ⊑ P ancestor+ ⊑ ancestor
functionalProperty T ⊑ ≤ 1P T ⊑ ≤ 1hasMother
inverseFunctionalProperty T ⊑ ≤ 1P⎺ T ⊑ ≤ 1hasSSN⎺

• The are many Ontology tools are available in the present
times such as
Protégé, OntoEdit, Ontolingua, OilEd, pOWL etc.

• Protégé is a free, open-source platform to construct domain
models and knowledge-based applications with ontologies.

• It provide Graphical User Interface for development of RDF
and OWL statement.

• Go http://protege.stanford.edu/download/registered.htmlto
download Protégé
• Protégé OWL editor is built with the full installation of
Protégé platform. During the install process, choose the
“Basic+OWL” option.
• For more details:
http://protege.stanford.edu/doc/owl/getting-started.html

Protégé

• There are two main ways of modeling ontologies:
– Frame-based
– OWL
• Each has its own user interface
– Protégé Frames editor: enables users to build and populate ontologies
that are frame-based, in accordance with OKBC (Open Knowledge
Base Connectivity Protocol).
– Protégé OWL editor: enables users to build ontology for the Semantic
Web, in particular to OWL
• Classes
• Properties
• Instances
• Reasoning

Building an OWL Ontology

Create a new OWL project
– Start protégé
– A new empty Protégé-OWL project has been created.
– Save it in your local file as pizza.owl

Named Classes

• Go to OWL Classes tab

• The empty class tree contains one class called owl:Thing, which is superclass of
everything.
• Create subclasses Pizza, PizzaTopping and PizzaBase. They are subclasses of
owl:Thing.

Disjoint classes

How to say that Pizza, PizzaTopping and PizzaBase classes are disjoint.

OWL Properties
• OWL Properties represent relationships between
two objects.
• There are two main properties:
– Object properties: link object to object
– datatype properties: link object to XML Schema
datatype or rdf:literal
• OWL has another property – Annotation
properties, to be used to add annotation
information to classes, individuals, OntoGraf etc.

Inverse Properties

• Each object property may have a corresponding
inverse property.
• If some property links individual a to individual
b, then its inverse property will link individual b
to individual a.

Functional Properties
• If a property is functional, for a given individual, there can
only be at most one individual to be related via this
property.
– For a given domain, range must be unique
• Functional properties are also known as single valued
properties.

Inverse Functional Properties

• If a property is inverse functional, then its inverse
property is functional.
– For a given range, domain must be unique.

Functional v/s Inverse Functional
Properties
• FunctionalProperty vs InverseFunctionalProperty

domain range example

Functional For a given Range is hasFather: A hasFather
Property domain unique B, A hasFather C B=C
InverseFunctional Domain is For a given hasID: A hasID B, C
Property unique range hasID B A=C

Transitive Properties
• If a property is transitive, and the property related individual
a to individual b, and also individual b to individual c, then
we can infer that individual a is related to individual c via
property P.

Symmetric Properties
• If a property P is symmetric, and the property relates
individual a to individual b, then individual b is also related
to individual a via property P.

Property: domains and ranges
• Properties link individuals from the domain to individuals
from the range

• Let us see the live demo in Protégé Software

Ontology Application
The topic can be discussed using two approaches:

• Discussing the Ontology application domains

• Discussing the Ontology integration in Applications (i.e. Context-aware Applications
using Ontology)

Ontology Application Domains/ Key
Areas
• Information retrieval procedure

• Knowledge representation/sharing

• Semantic Digital Libraries

• Software engineering

• Natural-Language processing

• Multi-agent systems

Information retrieval procedure
• Agricultural Ontology Service (AOS)
– The AOS/CS will serve as a multilingual repository of concepts
in the agricultural domain providing ontological relationships
and a rich, semantically sound terminology.
• the purpose of the AOS is to achieve:
• better indexing of resources,
• better retrieval of resources, and
• increased interaction within the agricultural community.

Information retrieval procedure

The Agricultural Ontology Service (AOS) (A Tool for Facilitating Access to Knowledge)
Food and Agriculture Organization of the United Nations (FAO)
Library and Documentation Systems Division, AGRIS/CARIS and Documentation Group
Rome, Italy, June 2001, Draft 5a, September 2001

Agricultural Ontology Service Concept
Server (AOS/CS)
• Initially developed using relational database

• Now new model is developed using Web Ontology Language (OWL)

• The new developed model in OWL will serve as a skeleton for building agriculture
domain ontologies.

*Lauser , B., Sini, M., Liang, A., Keizer, J. and Katz, S., “From AGROVOC to the Agricultural Ontology Service / Concept Server. An OWL
model for creating ontologies in the agricultural domain”, Networked Knowledge Organization Systems and Services, The 5th European
Networked Knowledge Organization Systems (NKOS) Workshop, Workshop at the 10th ECDL Conference, Alicante, Spain, September
21, 2006.

Server (AOS/CS)
• The multilingual issue (lexicalization) is handled using three levels
of representations i.e.
– Concepts (the abstract meaning),
– Term ( language-specific lexical form) and
– Term variant ( the range of forms that can occur for each term)

• On the Bases on the above representation inter-level relations are
defined i.e.
– Concept to Term (has_lexicalization)
– Term to String (has_acronym, has_spelling_variant, has_abbreviation)
– Concept to Concept (is_a)
– Term to Term (is_synonym_of, is_translation_of)

Server (AOS/CS)

The Basic Model

URI Disambiguation

The Concept-to-Concept
interface

Server (AOS/CS)

Term-to-Term Interface
Term-to-String Interface
Classification Schemes
 e.g. University of Bekkeley has the
following variants  Model has the support of two
l UCB, Cal, UC Berkeley, University of clasification schemes namely
Calfornia at Berkeley AGRIS/CARIS and FAO priority areas
l These relationships are modeled as l c_classification_scheme
properties of the data type l r_belongs_to_scheme
r_has_term_variant
l r_has_category
l r_has_sub_category

Semantic Digital Libraries*
• To provide uniform access to Digital Libraries to deal with structural and semantic
heterogeneities

Three application areas of ontologies (referred JeromeDL and BRICKS semantic digital
library projects)

– Bibliographic Ontologies
– Ontologies for Content Structures
– Community-aware Ontologies

*Kruk, R.S., Haslhofer, B., Piotrowski, Westerski, A. and Woroniecki, T. “The Role of Ontologies in Semantic Digital Libraries”, Networked
Knowledge Organization Systems and Services, The 5th European Networked Knowledge Organization Systems (NKOS)
Workshop, Workshop at the 10th ECDL Conference, Alicante, Spain, September 21, 2006.

Semantic Digital Libraries
• Ontologies for Content Structures
– By including structural concepts in ontologies, electronic
contents can be retrieved.

• Community-Aware Ontologies
– In semantic digital libraries, besides storing contents and meta
data, track of users, their interactions, and their knowledge
can be incorporated into the systems using community-aware
ontologies

Conclusion

Anurodh Kumar Sinha

Recent Developments
 Semantic Search
 Ontology Based Information Retrieval
1)Mental Model
2)User-Question Model
3)System Resource Model
4)System Query Model

 Semantic Digital libraries
1.Ontologies can be used to:
(i) organize bibliographic descriptions,
(ii) represent and expose document contents,
(iii) share knowledge amongst users

• Semantic Social Network
Social Network + Semantic Web
1)Social Layer
2)Ontology Layer
3) Concept Layer

Use of Ontology in Linked Data
 The IRW ontology can be used as a tool to make Linked Data more self-
describing and to allow inference to be used to test for membership in various
classes of resources
 The IRW ontology this in turn allows the semantic validation, to be able to
describe and infer in detail the types of resources that can be interacted with
via HTTP, which is useful for both tools like EARL that record validation of Web
standards to be implemented in a reliable fashion, which is useful for error-
reporting on the Web in general and HTTP in particular

 IRW clarifies the interactions between the hypertext Web and Linked
Data, allowing Linked Data spiders to keep track of important provenance
regarding the identity of resources, and to characterise the resources correctly
for semantic validation and error detection.

• Notion of consistency: The notion of consistency which is appropriate in this network
of ontologies in order to meet the requirements of future real-life application needs to
be analyze.

• Evolution of ontologies and metadata: One has to investigate which kind of metadata
are suitable for supporting the evolution of these network ontology.

• Reasoning: A basic open issue is the development of reasoning mechanisms in the
presence of inconsistencies between these networked ontology.

• Semi-automatic methods: Major obstacle to developing ontology-based application in
commercial setting. Therefore, the tight coupling of manual methods with automatic
methods is needed.

• Design patterns: Analogous to the development of design patterns in software
engineering of ontologies has to be improved by the development of pattern libraries
that provide ontology engineers with well engineered and application proven ontology
patterns that might be used on building block.

• Economic aspects: In commercial settings, one needs well-grounded estimations for
the effort one has to invest for building up the required ontologies in order to be able
to analyses and justify that investment. Up to now, only very preliminary methods exist
to cope with these economic aspect

Conclusion
• Ontologies enable a sound reasoning framework for making machines to be
contextual, discernable and relevant tool to produce semantic information
retrieval results

• Helps to reason and turn on the meaning in searching, i.e, thus add more
relevance in searching information

References
1. Amandeep S. Sidhu, Tharam S. Dillon,Fellow IEEE, Elizabeth Chang,Member
IEEE, Creating a Protein Ontology Resource
2.David Vallet, Miriam Fernández, and Pablo Castells, A n Ontology-Based
Information Retrieval Model
3. Fran¸cois Bry, Tim Furche, Paula-Lavinia Patranjan, and Sebastian Schaffert,
Data Retrieval and Evolution on the (Semantic) Web: A Deductive Approach
Protege Ontology Libraries
http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library
Protege tutorial
http://www.co-ode.org/resources/tutorials/
Protege Website
http://protege.stanford.edu/doc/users.html
http://protege.stanford.edu/

4.Guoqian Jiang, Katsuhiko Ogasawara, Naoki Nishimoto, Akira Endoh, Tsunetaro
Sakurai, FCAView Tab: A Concept-oriented View Generation Tool for
Clinical Data Using Formal Concept Analysis
5.G. Marcos, H. Eskudero, C. Lamsfus , M.T. Linaza, Data Retrieval From a
Cultural Knowledge Database
6. Jacob Köhler and Steffen Schulze-Kremer, The Semantic Metadatabase
(SEMEDA): Ontology based integration of federated molecular biological
data sources
7. Jeff Heflin and James Hendler, Searching the Web with SHOE

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