Semantic Webce.sharif.edu/courses/90-91/2/ce694-1/resources... · Ontologies in the Semantic Web Provide shared data structures to exchange information between agents. Can be explicitly

بسمه تعالی

Semantic Web

Morteza Amini

Ontology and OWL

Sharif University of Technology Spring 90-91

Outline

Introduction & Definitions

Ontology Languages

OWL (Ontology Web Language)

Sharif Univ. of Tech. RDF and RDF Schema - Morteza Amini 2

Outline


Ontology Languages



Where does it come from?

ontology n.

1692; lat. phil. onto- “being” + -logia “study of”

Philosophy

The study of what is, what has to be true for something to

exist, the kinds of things that can exist.

AI and computer science

Something exists if it can be represented, described, defined (in

a formal, hence, machine-interpretable way).

Sharif Univ. of Tech. Ontology and OWL - Morteza Amini 4

Ontologies


Ontologies (contd.)

Ontologies are about vocabularies and their meanings, with explicit, expressive, and well-defined semantics, possibly machine-interpretable.

“Ontology is a formal specification of a conceptualization.” Gruber, 1993

Main elements of an ontology:

Concepts

Relationships

Hierarchical

Logical

Properties

Instances (individuals)


A Definition

Informal

Terms

from a specific domain

uniquely defined, usually via natural language definitions

May contain additional semantics in the form of informal

relations.

Machine-processing is difficult.

Examples

Controlled vocabulary

Glossary

Thesaurus


A Definition

Formal

Domain-specific vocabulary.

Well-defined semantic structure

Classes/concepts/types

E.g., a class { Publication } represents all publications

E.g., a class { Publication } can have subclasses { Newspaper }, { Journal }

Instances/individuals/objects

E.g., the newspaper Le Monde is an instance of the class { Newspaper }

Properties/roles/slots

Data

E.g., the class { Publication } and its subclasses { Newspaper }, { Journal } have a data property { numberOfPages }

Object

E.g., the class { Publication } and its subclasses { Newspaper }, { Journal } have an object property { publishes }

Is machine-processable.


Ontologies in the Semantic Web

Provide shared data structures to exchange information between agents.

Can be explicitly used as annotations in web sites.

Can be used for knowledge-based services using other web resources.

Can help to structure knowledge to build domain models (for other purposes).

They are means of representing the semantics of documents and enabling the semantics to be used by web applications and intelligent agents.

Ontologies are critical for applications that want to search across or merge information from diverse communities.


Ontolgoies in the SW

Although XML DTDs and XML Schemas are sufficient for exchanging data between parties who have agreed to definitions beforehand, their lack of semantics prevent machines from reliably performing this task given new XML vocabularies.

RDF and RDF Schema begin to approach this problem by allowing simple semantics to be associated with identifiers. With RDF Schema, one can define classes that may have multiple subclasses and super classes, and can define properties, which may have sub properties, domains, and ranges.

In this sense, RDF Schema is a simple ontology language. However, in order to achieve interoperation between numerous, autonomously developed and managed schemas, richer semantics are needed.

For example, RDF Schema cannot specify that the Person and Car classes are disjoint, or that a string quartet has exactly four musicians as members.


Meaning is in Connections

W i

e

a

s

m

a

d

e

f

o

o

m

P

r

a

p

d

Paper

is made from

Wood


r

o

For Machines...

The meaning of the document is not defined.

Machines cannot understand it.

We are defining the structure of document by XML, but now the

meaning of the structure is not defined.


Wine is made from Grape

<Sentence>

<Subject>

Wine </Subject>

<Verb>

is made from </Verb>

<Object>

Grape

</Object>

</Sentence>

XM

L d

ocu

men

t

<Sentence>

<Subject>

Wine

</Subject>

<Verb>

is made from

</Verb>

<Object>

Grape

</Object>

</Sentence>

<Sentence>

<Subject>

</Subject>

<Verb>

</Verb>

<Object>

</Object>

</Sentence>

Wine

is made from

Grape

Ontology Gives the Meaning...

Document

Ontology

Natural Language


Why Develop Ontologies?

To share common understanding of the structure of

information among people or agents and share knowledge

E.g., using an ontology for integrating terminologies

To reuse domain knowledge

E.g., geography ontology

To make domain assumptions explicit

Facilitate knowledge management, easier to validate, to change, …

Enable new users to learn about the domain

….


What They Are Good for?

Search

Concept-based query

User uses own words, language

Related terms

Intelligent query expansion: “fishing vessels in China” expands to “fishing vessels in Asia”

Consistency checking

e.g., “Goods” has a property called “price” that has a value restriction of number

Interoperability support

Terms defined in expressive ontologies allow for mapping precisely how one term relates to another


Outline


Ontology Languages



Ontology Languages

Graphical notations

Semantic networks

Topic maps

UML

RDF

Logic based

Description Logics (e.g., OIL, DAML+OIL, OWL)

Rules (e.g., RuleML, LP/Prolog, SWRL)

First Order Logic


Ontology Languages

RDF(S) (Resource Description Framework (Schema))

OIL (Ontology Interchange Language)

DAML+OIL (DARPA Agent Markup Language + OIL)


XOL (XML-based Ontology Exchange Language)

SHOE (Simple HTML Ontology Extension)

OML (Ontology Markup Language)


Object Oriented Model

Many languages use object oriented model:

Objects/Instances/Individuals

Elements of the domain of discourse

Equivalent to constants in FOL

Types/Classes/Concepts

Sets of objects sharing certain characteristics

Equivalent to unary predicates in FOL and Concepts in DL

Relations/Properties/Roles

Sets of pairs (tuples) of objects

Equivalent to binary predicates in FOL and Roles in DL


Outline


Ontology Languages




OWL is now a W3C Recommendation.

The purpose of OWL is identical to RDFS i.e. to provide

an XML vocabulary to define classes, properties and their

relationships.

RDFS enables us to express very rudimentary relationships and

has limited inferencing capability.

OWL enables us to express much richer relationships, thus

yielding a much enhanced inferencing capability.

The benefit of OWL is that it facilitates a much greater

degree of inference than you get with RDF Schema.


Origins of OWL

RDF (S)

DAML+OIL

DARPA

Agent Markup

Language

A W3C

Recommendation

OIL

OWL

All

influenced

by RDF

Ontology

Inference

Layer

EU/NSF Joint Ad hoc

Committee

DAML

OWL Lite OWL DL OWL Full


OWL

OWL and RDF Schema enable rich machine-processable

semantics.

XML/DTD/XML Schemas

RDF Schema

OWL

Semantics

Syntax

<rdfs:Class rdf:ID="River">

<rdfs:subClassOf rdf:resource="#Stream"/>

</rdfs:Class>

<owl:Class rdf:ID="River">

<rdfs:subClassOf rdf:resource="#Stream"/>

</owl:Class>

RDFS

OWL


Why Build on RDF

Provides basic ontological primitives

Classes and relations (properties)

Class (and property) hierarchy

Can exploit existing RDF infrastructure

Provides mechanism for using ontologies

RDF triples assert facts about resources

Use vocabulary from DAML+OIL ontologies


OWL Design Goals

Shared ontologies

Ontology interoperability

Inconsistency detection

Expressivity vs. scalability

Ease of use

Compatibility with other standards

Internationalization


Versions of OWL

Depending on the intended usage, OWL provides three

increasingly expressive sublanguages.

Full

Very expressive, no computation guarantees.

DL (Description Logic)

Maximum expressiveness, computationally

complete.

Lite

Simple classification hierarchy with simple

constraints.

OWL Full

OWL DL

OWL Lite


Comparison of Versions

OWL Lite supports those users primarily needing a classification hierarchy and simple constraints. E.g., while it supports cardinality constraints, it only permits cardinality values of 0 or 1

OWL DL supports those users who want the maximum expressiveness while retaining decidability. OWL DL includes all OWL language constructs, but they can be used only under certain restrictions. E.g., while a class may be a subclass of many classes, a class cannot be an instance of another class). OWL DL is so named due to its correspondence with description logics.

OWL Full is meant for users who want maximum expressiveness and the syntactic freedom of RDF with no computational guarantees. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right.


Describing Classes in OWL

OWL vs. RDFS

OWL allows greater expressiveness

Abstraction mechanism to group resources with similar

characteristics

Much more powerful in describing constraints on relations

between classes

Property transitivity, equivalence, symmetry, etc.

…

Extensive support for reasoning


OWL Ontologies

What’s inside an OWL ontology

Classes + class-hierarchy

Properties (Slots) / values

Relations between classes

(inheritance, disjoints, equivalents)

Restrictions on properties (type, cardinality)

Characteristics of properties (transitive, …)

Annotations

Individuals

Reasoning tasks: classification, consistency checking


Classes

What is a Class?

e.g., person, pet, old

a collection of individuals (object, things, . . . )

a way of describing part of the world

an object in the world (OWL Full)


owl:Class

Sub class of Class in RDF

Better to forget about classes of classes

Top-most class: owl:Thing

<owl:Class rdf:ID=“Person"/>

<owl:Class rdf:ID=“Man">

<rdfs:subClassOf rdf:resource="#Person" />

</owl:Class>


Individuals

Two equivalent declarations:

<Person rdf:ID=“ahmadi" />

<owl:Thing rdf:ID=“ahmadi" />

<owl:Thing rdf:about="#ahmadi">

<rdf:type rdf:resource="#Person"/>

</owl:Thing>


Properties

What is a Property?

e.g., has_father, has_pet, service_number

a collection of relationships between individuals (and data)

a way of describing a kind of relationship between individuals

an object in the world (OWL Full)


OWL Properties

Object

Properties

Ana owns Cuba

Is range a

literal / typed value ?

then ERROR

Data type

Properties

Ana age 25

XML Schema data types

supported


Defining Properties

owl:ObjectProperty

owl:DatatypeProperty

rdfs:subPropertyOf

rdfs:domain

rdfs:range

<owl:ObjectProperty rdf:ID="madeFrom"> <rdfs:domain rdf:resource="#Book"/> <rdfs:range rdf:resource="#Paper"/> </owl:ObjectProperty>



Complex Classes

Union of classes (owl:unionOf) OR (A B)

Union of classes (owl:intersectionOf) AND (A B)

Complement (owl:complementOf) NOT (A)

Enumeration (owl:oneOf)

Disjoint Classes (owl:disjointWith)



Property Restrictions

Defining a Class by restricting its possible instances via their

property values

OWL distinguishes between the following two:

Value constraint

Cardinality constraint



Restrictions on Property Classes

Properties:

allValuesFrom: rdfs:Class (lite/DL owl:Class)

hasValue: specific Individual

someValuesFrom: rdfs:Class (lite/DL owl:Class)

minCardinality: xsd:nonNegativeInteger (in lite {0,1})

maxCardinality: xsd:nonNegativeInteger (in lite {0,1})


What’s in OWL, but not in RDF

Ability to be distributed across many systems

Scalable to Web needs

Compatible with Web standards for:

accessibility, and

Internationalization

Open and extensible


Describing Properties in OWL

OWL vs. RDFS

RDF Schema provides some of predefined properties: rdfs:range used to indicate the range of values for a property.

rdfs:domain used to associate a property with a class.

rdfs:subPropertyOf used to specialize a property.

…

OWL provides additional predefined properties: owl:cardinality (indicate cardinality)

owl:hasValue (at least one of the specified property values)

…

OWL provides additional property classes, which allow reasoning and inferencing: owl:FunctionalProperty

owl:TransitiveProperty

…


Describing Properties in OWL

OWL Property Classes

An ObjectProperty relates one Resource to another

Resource.

A DatatypeProperty relates one Resource to a Literal - an

XML Schema data type.

rdf:Property

owl:ObjectProperty owl:DatatypeProperty owl:FunctionalProperty owl:InverseFunctionalProperty

owl:SymmetricProperty owl:TransitiveProperty


Transitivity of properties

X p1 Y

Y p1 Z

implies X p1 Z

Transitivity existed already in RDF

“subClassOf”, and ???

e.g. located_in, part_of


Symmetric properties

X p1 Y

implies X p1 Y

e.g., =


Functional Properties

X p1 Y

X p1 Z

imply Z is the same as Y

(they describe the same)

What if Y, Z

where explicitly defined as “different” ?


Inverse Functional Properties

Y p1 A

Z p1 A

imply Z is the same as Y

(they describe the same)

What if Y, Z

where explicitly defined as “different” ?


OWL distributed

“equivalent class”

“equivalent Property”

Guitar

Guitarra

Internationalization standards ?


Complex Classes

Male

Students

Married Female Professors

Married Female Students

Divorced

Female

Human

Student

Married

Professor

Minority Students example


Disjoint Classes

Married disjoint with:

Divorced

Widowed

Single

Are “Divorced” and “Single” disjoint ?

Married

Widowed Divorced Single


OWL Cardinality

min Cardinality

max Cardinality

“Cardinality”

When min = max

has Value

belongs to the class if it has the value


An Example OWL Ontology

<owl:Class rdf:ID=“Person” />

<owl:Class rdf:ID=“Man”> <rdfs:subClassOf rdf:resource=“#Person” />

<owl:disjointWith rdf:resource=“#Woman” />

</owl:Class>

<owl:Class rdf:ID=“Woman”>

<rdfs:subClassOf rdf:resource=“#Person” />

<owl:disjointWith rdf:resource=“#Man” />

</owl:Class>

<owl:Class rdf:ID=“Father”>

<rdfs:subClassOf rdf:resource=“Man” />

<owl:Restriction owl:minCardinality="1">

<owl:onProperty rdf:resource="#hasChild" />

</owl:Restriction>

</owl:Class>

<owl:ObjectProperty rdf:ID=“hasChild">

<rdfs:domain rdf:resource="#Parent" />

<rdfs:range rdf:resource="#Person" />

</owl:ObjectProperty>


Use Cases – Web Portals (1)

A web portal is a web site that provides information content on a common topic, for example a specific city or domain of interest.

A web portal allows individuals that are interested in the topic to receive news, find and talk to one another, build a community, and find links to other web resources of common interest.

In order for a portal to be successful, it must be a starting place for locating interesting content.

Typically this content is submitted by members of the community, who often index it under some subtopic.

Another means of collecting content relies on the content providers tagging the content with information that can be used in syndicating it. Typically, this takes the form of simple metatags that identify the topic of the content, etc.


Use Cases – Web Portals (2)

However, a simple index of subject areas may not provide the community with sufficient ability to search for the content that its members require.

In order to allow more intelligent syndication, web portals can define an ontology for the community. This ontology can provide a terminology for describing content and axioms that define terms using other terms from the ontology.

For example, an ontology might include terminology such as "journal paper," "publication," "person," and "author.“

This ontology could include definitions that state things such as "all journal papers are publications" or "the authors of all publications are people."

When combined with facts, these definitions allow other facts that are necessarily true to be inferred. These inferences can, in turn, allow users to obtain search results from the portal that are impossible to obtain from conventional retrieval systems.

Examples of Semantic Web Portals are OntoWeb and The Open Directory Portal.


Use Cases – Multimedia Collections (1)

Ontologies can be used to provide semantic annotations for

collections of images, audio, or other non-textual objects.

these types of resources are typically indexed by captions or

metatags. However, since different people can describe these

non-textual objects in different ways, it is important that the

search facilities go beyond simple keyword matching.

Multimedia ontologies can be of two types: media-specific and

content-specific.

As an example of a multimedia collection, consider an archive

of images of antique furniture. An ontology of antique furniture

would be of great use in searching such an archive.


Use Cases – Multimedia Collections (2)

A taxonomy can be used to classify the different types of furniture.

It would also be useful if the ontology could express definitional knowledge. For example, if an indexer selects the value "Late Georgian" for the style/period of (say) an antique chest of drawers, it should be possible to infer that the data element "date.created" should have a value between 1760 and 1811 A.D. and that the "culture" is British.

Availability of this type of background knowledge significantly increases the support that can be given for indexing as well as for search.


Use Cases – Corporate Web Site Management (1)

Large corporations typically have numerous web pages concerning things like press releases, product offerings and case studies, corporate procedures, internal product briefings and comparisons, white papers, and process descriptions.

Ontologies can be used to index these documents and provide better means of retrieval.

A single ontology is often limiting because the constituent categories are likely constrained to those representing one view and one granularity of a domain

An ontology-enabled web site may be used by: A salesperson looking for sales collateral relevant to a sales pursuit.

A technical person looking for pockets of specific technical expertise and detailed past experience.

A project leader looking for past experience and templates to support a complex, multi-phase project, both during the proposal phase and during execution.


A typical problem for each of these types of users is that they

may not share terminology with the authors of the desired

content.

The salesperson may not know the technical name for a desired

feature

For such problems, it would be useful for each class of user to

have different ontologies of terms, but have each ontology

interrelated so translations can be performed automatically.

Another problem is framing queries at the right level of

abstraction. A project leader looking for someone with

expertise in operating systems should be able to locate an

employee who is an expert with both Unix and Windows.


Use Cases – Corporate Web Site Management (2)

Use Cases – Design Documentation

A concrete example of this use case is design documentation for the aerospace domain, where typical users include:

Maintenance engineer looking for all information relating to a particular part (e.g., "wing-spar").

Design engineer looking at constraints on re-use of a particular sub-assembly.

To support this kind of usage, it is important that constraints can be defined. These constraints may be used to enhance search or check consistency. An example of a constraint might be:

biplane(X) => CardinalityOf(wing(X)) = 2

wingspar(X) AND wing(Y) AND isComponentOf(X,Y) => length(X) < length(Y)


Use Cases – Agents and Services (1)

The Semantic Web can provide agents with the capability to understand and integrate diverse information resources.

A specific example is that of a social activities planner, which can take the preferences of a user (such as what kinds of films they like, what kind of food they like to eat, etc.) and use this information to plan the user's activities for an evening.

This type of agent requires domain ontologies that represent the terms for restaurants, hotels, etc. and service ontologies to represent the terms used in the actual services. These ontologies will enable the capture of information necessary for applications to discriminate and balance among user preferences. Such information may be provided by a number of sources, such as portals, service-specific sites, reservation sites and the general Web.


http://www.agentcities.org/

Use Cases – Agents and Services (2)

Agentcities is an example of an initiative that is exploring the use of agents in a distributed service environment across the Internet. This will involve building a network of agent platforms that represent real or virtual cities, such as San Francisco or the Bay Area, and populating them with the services of those cities.

This will require a number of different domain and service ontologies: Key issues include: Use and integration of multiple separate ontologies across different

domains and services

Distributed location of ontologies across the Internet

Potentially different ontologies for each domain or service (ontology translation/cross-referencing)

Simple ontology representation to make the task of defining and using ontologies easier


http://www.agentcities.org/

http://sf.us.agentcities.net/

Use Cases – Ubiquitous Computing (1)

Ubiquitous computing is an emerging paradigm of personal computing, characterized by the shift from dedicated computing machinery to pervasive computing capabilities embedded in our everyday environments.

The pervasiveness and the wireless nature of devices require network architectures to support automatic, ad hoc configuration.

A key technology of true ad hoc networks is service discovery, functionality by which "services" can be described, advertised, and discovered by others.

The key issue (and goal) of ubiquitous computing is that devices which weren't necessarily designed to work together should be able to discover each others' functionality and be able to take advantage of it.


Use Cases – Ubiquitous Computing (2)

Ubiquitous computing is an emerging paradigm of personal computing, characterized by the shift from dedicated computing machinery to pervasive computing capabilities embedded in our everyday environments.

The pervasiveness and the wireless nature of devices require network architectures to support automatic, ad hoc configuration.

A key technology of true ad hoc networks is service discovery, functionality by which "services" can be described, advertised, and discovered by others.

The key issue (and goal) of ubiquitous computing is that devices which weren't necessarily designed to work together should be able to discover each others' functionality and be able to take advantage of it.


References

http://www.w3.org/TR/owl-ref/

Chapter 6 of the book







Any Question...

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Semantic Webce.sharif.edu/courses/90-91/2/ce694-1/resources... · Ontologies in the Semantic Web Provide shared data structures to exchange information between agents. Can be explicitly