Working with Ontologies

Introduction to DOGMA and related research

Outline

Ontology

DOGMA

Semantic Web

Issues

Ontology Definition

“Classical” definition: “Specification of a conceptualization”

Keyword: AgreementSemantic consistency

Unambiguous communication

Ontology Paradigms

LogicA priori specificationFormal logicNecessarily Small-scale

ModelingFocus on applicationFormal basisPotentially large-scale

Ontology Paradigms

Extensional vs. Intensional

IntensionalStrongly based on axioms and rules

Hard agreement

ExtensionalLarge collections of facts

Scalablility

Ontology and IS Semantics

ConceptualSchema

agreement

ONTOLOGY

designer

domain expert

user

Any Design

Tool

Implementation

Information System (including

the WWW)

interpretation

Data

“World”

Ontology Grail

“specification of interface, communication and documentation for any module in any software system is mapable to a common ontology”

[Meersman 2000]

Outline

Ontology

DOGMA

Semantic Web

Issues

DOGMA Purpose

STARLab Ontology experimentation platform

Flexible, modular architecture

Lexon-based metamodel

Ontology Server generator

DOGMA Architecture

DOGMA Metamodel

Lexons: elements of form

<t0 r t>

where is a context; t0, t are terms and r is a role

DOGMA metamodel

Example:(#my_company) employee

is_a (#living_being) personis_a contract_partyWITH first_nameWITH last_nameWITH empl-idhas_birth datehas_start datehas salaryworks_in department

DOGMA metamodel

DOGMA Syntax

XML-based representation of the model.

Bulk conversion of ontologies:Conversion of existing ontology to DOGMA syntax

Bulk insertion in a separate context

(Semi-)Manual alignment

DOGMA API

Programmatic access to the ontology for clients

Java 2 API

Direct support of the metamodel

Basic operations support

DOGMA Content

Incorporation of well-known thesaurusWordNet

Project-specific content]EuroWordnet base types

IPTC Category System

….

DOGMA Applications

Generation of application-specific “views” on the global ontology

Delivery of support applications(Tailored) Browsers/Editors

DOGMA Projects:Hypermuseum

NAMIC

DOGMA Applications: HMHypermuseum projectPurpose: To create a tool for the creation of websites to browse of museum informationOntology-supported navigation and searching of appropriate museum dataOntology sources:

Models from museumsData from museumsWordNet

DOGMA Applications: NAMIC

News processing project

Purpose: Support of journalists in news agencies

Project-wide ontology-based semanticsOntology service

User profiling

DOGMA Applications: NAMICE nglis h Ital ian Spanis h

Englis h LP Italian LP S p anis h LP

B uild ing ofM onolingua l

H ype rT e xt D B(I ta lia n)

B uild ing ofM onolingua l

H ype rT e xt D B(S pa nis h)

C ros sL inguis tic

N e w s L inking

B uild ing ofM onolingua l

H ype rT e xt D B(E nglis h)

W P 5

W P 6

M ultilingua lH ype r-N e w s

E ngine

W P 7

G U I

WP

4

U s e r a ndD om a inP rof ile

W P 3

P ro f i le s

DOGMA Applications: NAMIC

Merged ontological resourcesNews categories (IPTC)

Lexical resources• EuroWordNet

• Named Entities

User profilingDetermine the user’s information needs

Provide a consistent view of the system for developers and users

Outline

Ontology

DOGMA

Semantic Web

Issues

Semantic Web Introduction“The Web was designed as an information space, with the goal that it should be useful not only for human-human communication, but also that machines would be able to participate and help. One of the major obstacles to this has been the fact that most information on the Web is designed for human consumption […] the Semantic Web approach instead develops languages for expressing information in a machine processable form.”

http://www.w3.org/DesignIssues/Semantic.html

Semantic Web Syntactic level

XML: General syntactic infrastructure

Arbitrary document types defined by DTD (or XML Schema)

Related standardsNamespaces

Linking

….

Semantic Web Vocabulary level

RDF(S)

Topic Maps

Semantic Web Vocabulary level

Semantic Web Vocabulary level<rdf:RDF>

<rdf:Description about="http://mycollege.edu/courses/6.001">

<s:students>

<rdf:Bag>

<rdf:li resource="http://mycollege.edu/students/Amy"/> <rdf:li resource="http://mycollege.edu/students/Tim"/> <rdf:li resource="http://mycollege.edu/students/John"/> <rdf:li resource="http://mycollege.edu/students/Mary"/> <rdf:li resource="http://mycollege.edu/students/Sue"/> </rdf:Bag>

</s:students>

</rdf:Description>

</rdf:RDF>

Semantic Web Vocabulary level

RDF SchemaClasses and properties

Constrains

Extensibility

Semantic Web Vocabulary level

Semantic Web Logical level

Very much in progress

Some prototype languages and systems

Fundamental scalability problems

Semantic Web and DOGMA

Similar assertion-based metamodels

Possibility of using DOGMA as a repository for Ontologies in the Semantic Web

Outline

Ontology

DOGMA

Semantic Web

Issues

Future work

Alignment

Visualization

Mining

Semantic Web Convergence

Alignment concepts

Merging: To create a single coherent ontology that includes all the information form all sources

Alignment: To make the all sources consistent and coherent with one another but keep them separate

Alignment algorithms

PROMPT: Semiautomatic, semantic-based algorithm

Simple frame-based knowledge model:Classes

Slots

Facets

Instances

Alignment algorithms: PROMPT

Make initial suggestions

Select next operation

Perform automatic updates

Find conflicts

Make suggestions

Alignment algorithms: PROMPT

Alignment algorithms: PROMPT

Mining

Content availability is a major issue

Sources:Conceptual schemas

Database schemas

XML DTD’s and schemas

Semantic web

….

Issues and DOGMA

Aligment: Direct support (and better algorithms) needed

Mining: DOGMA model allows quick incorporation of new ontology data

Visualization: Potential large-scale ontologies may require new techniques

Projects available!

http://starlab.vub.ac.be