McGuinness COGNA October 3, 2003

Ontologies: What you should know and why you might care

Deborah McGuinness

Associate Director and Senior Research Scientist

Knowledge Systems Laboratory

Stanford University

Stanford, CA USA

dlm@ksl.stanford.edu

http://www.ksl.stanford.edu/people/dlm

McGuinness COGNA October 3, 2003

What is an Ontology?

Catalog/ID

GeneralDescription

Logics

Terms/glossary

Thesauri“narrower

term”relation

Formaltaxonomy

Frames(properties)

Term Hierarchy

(e.g. Yahoo!)

Formalinstance

Value Restrs.

General Logic

*based on AAAI ’99 Ontologies panel – Gruninger, Lehmann, McGuinness, Uschold, WeltyUpdated by McGuinness, additional input from Gruninger, Uschold, and Rockmore

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General Nature of Descriptionsa WINE

a LIQUIDa POTABLE

grape: chardonnay, ... [>= 1]sugar-content: dry, sweet, off-drycolor: red, white, roseprice: a PRICEwinery: a WINERY

grape dictates color (modulo skin)harvest time and sugar are related

general categories

structured components

interconnectionsbetween parts

number/card restrictions

valuerestrictions

class

superclass

Roles/properties

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Some uses of OntologiesSimple ontologies (taxonomies) provide:• Controlled shared vocabulary (search engines, authors,

users, databases, programs/agents all speak same language)• Site Organization, Navigation Support, Expectation setting• “Umbrella” Upper Level Structures (for extension e.g.,

UNSPSC)• Browsing support (tagged structures such as Yahoo!)• Search support (query expansion approaches such as

FindUR, e-Cyc; structured search)• Sense disambiguation (e.g., TAP)

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Semantic Web Vision

Today’s web enriched with information encoding term meaning enabling applications that are:

• Able to understand term meaning and user background• Interoperable (can translate between applications and

vocabularies)• Programmable (thus agent operational)• Explainable (thus maintains context and can adapt)• Capable of filtering (thus limiting display and human

intervention requirements)• Capable of executing services

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Semantic Enablers

• Languages for representing terms in vocabularies• Tools for generating, maintaining, and evolving

ontologies• Tools for reasoning with and using semantically

enhanced applications

Facilitated by W3C, Govt - DARPA, ARDA, NSF, NIST, EU, …

McGuinness COGNA October 3, 2003

DARPA’s DAML/W3C’s OWL Language

Web LanguagesRDF/SXML

DAML-ONT

Formal FoundationsDescription Logics

FACT, CLASSIC, DLP, …

Frame Systems

DAML+OIL(OWL)

OIL

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Ontology Resources…• Upper Level Ontologies- UNSPSC, SUMO, OpenCyc, OpenDirectory, TAP, …• Specialized Ontologies (Many beyond just GML)

– Geography Ontology CIA World Fact Book geographic regions; WFB climate data interpreted using Koeppen Climate Classification system- www.fao.org/WAICENT/FAOINFO/sustdev/EIdirect/climate/EIsp0002.htm 3. Sea Level definitions - www.pol.ac.uk/psmsl/puscience/index.html

– Geography Ontology - geographical ontology & theory in FOL capable of accessing and utilizing information from a variety of agents, including Alexandria Digital Library Gazetteer, TerraVision, the CIA World Factbook, Teknowledge's ASCS, and Landsat and GDACC satellite data repositories. Using axiomatic characterizations of these agents’ capabilities, in conjunction with SNARK's procedural-attachment mechanism and the OAA agent library, the combined theory is capable of finding answers that must be inferred from more than one of these sources because no one source has the entire answer

• Ontology Libraries– http://www.daml.org/ontologies/– http://www.ksl.stanford.edu/ontolingua

• “Advisory” bodies - Semantic Web Science Foundation, NIST, Ontology.org• Ontology Consultants

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Ontology ToolsTools developing: http://www.daml.org/tools/ and

http://www.w3.org/2001/sw/WebOnt/impls#Implementations

Annotation Ontology TranslationBrowser PersistenceCrawler Query ToolsEditor RDMS MappingGraph Visualizer Report GenerationTransformation Search Validator Ontology AnalyzerImporter Ontology EditorInference Engine Merging

Many are in research labs, but companies emerging and lasting…Network Inference, Sandpiper, Ontoprise, AppliedSemantics, Sentius, ….

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Conclusion/Discussion• Ontologies are taking off in terms of languages,

tools, environments, and applications• Rich representation languages exist for

representing taxonomies, thesauri, and beyond. • Transition paths exist from standard languages

such as XML to other web standards like RDF and OWL

• Ontology toolkits for ontology building, evolution, merging, etc. exist today and are growing quickly (academics, government, and industry)

• Ontology libraries exist and are worth considering for leverage, connections, and merging

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PointersSelected Papers:- McGuinness. Ontologies come of age, 2003- Das, Wei, McGuinness, Industrial Strength Ontology Evolution Environments, 2002.- Kendall, Dutra, McGuinness. Towards a Commercial Strength Ontology Development Environment, 2002.- McGuinness Description Logics Emerge from Ivory Towers, 2001.- McGuinness. Ontologies and Online Commerce, 2001.- McGuinness. Conceptual Modeling for Distributed Ontology Environments, 2000.- McGuinness, Fikes, Rice, Wilder. An Environment for Merging and Testing Large Ontologies, 2000.- Brachman, Borgida, McGuinness, Patel-Schneider. Knowledge Representation meets Reality, 1999.- McGuinness. Ontological Issues for Knowledge-Enhanced Search, 1998.- McGuinness and Wright. Conceptual Modeling for Configuration, 1998.

Selected Tutorials:-Smith, Welty, McGuinness. OWL Web Ontology Language Guide, 2003.-Noy, McGuinness. Ontology Development 101: A Guide to Creating your First Ontology. 2001.- Brachman, McGuinness, Resnick, Borgida. How and When to Use a KL-ONE-like System, 1991.

Languages, Environments, Software:- OWL - http://www.w3.org/TR/owl-features/ , http://www.w3.org/TR/owl-guide/- DAML+OIL: http://www.daml.org/- Inference Web - http://www.ksl.stanford.edu/software/iw/ - Chimaera - http://www.ksl.stanford.edu/software/chimaera/ - FindUR - http://www.research.att.com/people/~dlm/findur/ - TAP – http://tap.stanford.edu/- DQL - http://www.ksl.stanford.edu/projects/dql/

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EXTRAS

McGuinness COGNA October 3, 2003

OWL Lite Features • RDF Schema Features

– Class, rdfs:subClassOf , Individual – rdf:Property, rdfs:subPropertyOf – rdfs:domain , rdfs:range

• Equality and Inequality– sameClassAs , samePropertyAs , sameIndividualAs – differentIndividualFrom

• Restricted Cardinality – minCardinality, maxCardinality (restricted to 0 or 1) – cardinality (restricted to 0 or 1)

• Property Characteristics– inverseOf , TransitiveProperty , SymmetricProperty – FunctionalProperty(unique) , InverseFunctionalProperty– allValuesFrom, someValuesFrom (universal and existential local range

restrictions)• Datatypes

– Following the decisions of RDF Core. • Header Information

– imports , Dublin Core Metadata , versionInfo

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OWL Features

• Class Axioms– oneOf (enumerated classes) – disjointWith – sameClassAs applied to class expressions – rdfs:subClassOf applied to class expressions

• Boolean Combinations of Class Expressions – unionOf – intersectionOf – complementOf

• Arbitrary Cardinality – minCardinality – maxCardinality – cardinality

• Filler Information– hasValue Descriptions can include specific value information

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Chimaera: Ontology Environment Tool

An interactive web-based tool aimed at supporting:•Ontology analysis (correctness, completeness, style, …)•Merging of ontological terms from varied sources•Maintaining ontologies over time•Validation of input

• Features: multiple I/O languages, loading and merging into multiple namespaces, collaborative distributed environment support, integrated browsing/editing environment, extensible diagnostic rule language

• Used in commercial and academic environments, basis of some

commercial re-implementations (Ontobuilder/Ontoserver, …)

• Available as a hosted service from www-ksl-svc.stanford.edu

• Information: www.ksl.stanford.edu/software/chimaera

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Layer Cake Foundation

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XML• World Wide Web Consortium (W3C)

standard• Provides important solution to syntax

problem and simple semantics and schemas:

<SSN>555-17-1234</SSN>

• Now we can describe the meaning of words• Many applications of XML appearing:

– Geographic Markup Language (GML)– Extensible rights Markup Language (XrML)– Chemical Markup Language (CML)

Problem: Limited semantics, limited ontology creation

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DARPA Agent Markup Language

• http://www.daml.org/about.html

• Extends the vocabulary of XML and RDF/S

• Provides rich ontology representation language

• Language features chosen so language may have efficient implementations

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DAML+OIL -> W3C

• W3C Webont working group formed with DAML+OIL submission as starting point http://www.w3.org/Submission/2001/12/

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WEBONT participation….

• Includes over 50 members from over 30 organizations. – Industry including:

• Large companies such as Daimler Chrysler, EDS, Fujitsu, HP, IBM, Intel, Lucent, Nokia, Philips Electronics, Sun, Unisys, …

• Newer/smaller companies such as IVIS Group, Network Inference, Stilo Technology, Unicorn Solutions, …

– Government and Not-For-Profits:• Defense Information Systems Agency, Interoperability Technology

Association for Information Processing, Japan (INTAP) , Intelink Mgt Office, Mitre, …

– Universities and Research Centers:• University of Bristol, University of Maryland, University of Southamptom,

Stanford University, …• DFKI (German Research Center for Artificial Intelligence),

Forschungszentrum Informatik– Invited Experts

• Well-known academics from non-W3C members

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Simple Ontology-Enhanced Apps

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McGuinness COGNA October 3, 2003

Today: Rich Information Source for Human Manipulation/Interpretation

Human

Human

McGuinness COGNA October 3, 2003

“I know what was input”

• Global documents and terms indexed and available for search• Search engine interfaces• Entire documents retrieved according to relevance (instead of

answers)• Human input, review, assimilation, integration, action, etc.• Special purpose interfaces required for user friendly applications

The web knows what was input but does little interpretation, manipulation, integration, and action.

Analogous to a new assistant who is thorough yet lacks common sense, context, and adaptability

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Tomorrow: Rich Information Source for Agent Manipulation/Interpretation

Human

Agent

Agent

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“I know what was meant”

• Understand term meaning and user background• Interoperable (can translate between applications)• Programmable (thus agent operational)• Explainable (thus maintains context and can adapt)• Capable of filtering (thus limiting display and

human intervention requirements)• Capable of executing services

McGuinness COGNA October 3, 2003

Contact Information

dlm@ksl.stanford.edu

www.ksl.stanford.edu/people/dlm