Ontologies Come of Age Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford,

Ontologies Come of Ontologies Come of AgeAge

Deborah L. McGuinnessDeborah L. McGuinnessAssociate Director and Senior Associate Director and Senior

Research ScientistResearch ScientistKnowledge Systems LaboratoryKnowledge Systems Laboratory

Stanford UniversityStanford UniversityStanford, CA 94305Stanford, CA 94305

650-723-9770650-723-9770 [email protected]@ksl.stanford.edu

June 12, 2003 2McGuinness - Mitre

What is an What is an Ontology?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


General Nature of General Nature of DescriptionsDescriptions

a 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


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

authors, users, databases, programs/agents all speak authors, users, databases, programs/agents all speak same language)same language)

Site Organization, Navigation Support, Expectation Site Organization, Navigation Support, Expectation settingsetting

““Umbrella” Upper Level Structures (for extension Umbrella” Upper Level Structures (for extension e.g., UNSPSC)e.g., UNSPSC)

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

FindUR, e-Cyc)FindUR, e-Cyc) Sense disambiguation (e.g., TAP)Sense disambiguation (e.g., TAP)


FindUR ArchitectureFindUR Architecture

SearchEngine

Content to Search:

Search Technology:

User Interface:

Verity (and topic sets)

Content (WebPages or Databases

CLASSIC Knowledge Representation System

Results(domain specific)

Verity SearchScript, Javascript, HTML, CGI, CLASSIC

Content

Classification

Domain

Knowledge

Results(standard format)

GUI supporting browsing

and selection

Research SiteTechnical MemorandumCalendars (Summit 2005, Research) Yellow Pages (Directory Westfield)Newspapers (Leader) Internal Sites (Rapid Prototyping) AT&T Solutions Worldnet Customer Care Medical Information

Domain

Knowledge

Collaborative Topic Set Tool






Uses of Ontologies IIUses of Ontologies II Consistency CheckingConsistency Checking CompletionCompletion Interoperability SupportInteroperability Support Support for validation and verification Support for validation and verification

testing (e.g. testing (e.g. http://ksl.stanford.edu/projects/DAML/chimahttp://ksl.stanford.edu/projects/DAML/chimaera-jtp-cardinality-test1.damlera-jtp-cardinality-test1.daml ) )

Configuration supportConfiguration support Structured, “surgical” comparative Structured, “surgical” comparative

customized searchcustomized search Generalization / SpecializationGeneralization / Specialization … … Foundation for expansion and leverageFoundation for expansion and leverage


KSL Wine AgentKSL Wine AgentSemantic Web IntegrationSemantic Web Integration

Wine Agent receives a meal description and retrieves a selection of matching wines available on the Web, using an ensemble of emerging standards and tools:

• DAML+OIL / OWL for representing a domain ontology of foods, wines, their properties, and relationships between them• JTP theorem prover for deriving appropriate pairings• DQL for querying a knowledge base consisting of the above• Inference Web for explaining and validating the response• [Web Services for interfacing with vendors]• Utilities for conducting and caching the above transactions



ProcessingProcessing Given a description of a meal,Given a description of a meal,

Use DQL to state a premise (the meal) and query the Use DQL to state a premise (the meal) and query the knowledge base for a suggestion for a wine description knowledge base for a suggestion for a wine description or set of instancesor set of instances

Use JTP Theorem Prover to deduce answers (and Use JTP Theorem Prover to deduce answers (and proofs)proofs)

Use Inference Web to explain results (descriptions, Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.)instances, provenance, reasoning engines, etc.)

Access relevant web sites (wine.com, wine commune, Access relevant web sites (wine.com, wine commune, …) to access current information…) to access current information

Use DAML-S for markup and protocol*Use DAML-S for markup and protocol*

http://www.ksl.stanford.edu/projects/wine/explanation.html




Querying multiple online Querying multiple online sourcessources


A Few Observations about A Few Observations about OntologiesOntologies Simple ontologies can be built by non-expertsSimple ontologies can be built by non-experts

Verity’s Topic Editor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-Verity’s Topic Editor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-ED, etc.ED, etc.

Ontologies can be semi-automatically generatedOntologies can be semi-automatically generated from crawls of site such as yahoo!, amazon, excite, etc.from crawls of site such as yahoo!, amazon, excite, etc. Semi-structured sites can provide starting pointsSemi-structured sites can provide starting points

Ontologies are exploding (business pull instead of technology push)Ontologies are exploding (business pull instead of technology push) e-commerce - MySimon, Amazon, Yahoo! Shopping, VerticalNet, …e-commerce - MySimon, Amazon, Yahoo! Shopping, VerticalNet, … Controlled vocabularies (for the web) abound - SIC codes, UMLS, UNSPSC, Controlled vocabularies (for the web) abound - SIC codes, UMLS, UNSPSC,

Open Directory (DMOZ), Rosetta Net, SUMOOpen Directory (DMOZ), Rosetta Net, SUMO Business interest expanding – ontology directors, business ontologies are Business interest expanding – ontology directors, business ontologies are

becoming more complicated (roles, value restrictions, …), VC firms interested,becoming more complicated (roles, value restrictions, …), VC firms interested, Markup Languages growing XML, RDF, DAML, RuleML, xxMLMarkup Languages growing XML, RDF, DAML, RuleML, xxML ““Real” ontologies are becoming more central to applicationsReal” ontologies are becoming more central to applications Search companies moving towards them – Yahoo, recently GoogleSearch companies moving towards them – Yahoo, recently Google




Implications and Needs Implications and Needs for Ontology-enhanced for Ontology-enhanced

applicationsapplications Ontology Language Syntax and Semantics Ontology Language Syntax and Semantics

(DAML+OIL, OWL)(DAML+OIL, OWL) Upper Level/Core ontologies for reuse (Cyc, SUMO, Upper Level/Core ontologies for reuse (Cyc, SUMO,

CNS coalition, DAML-S…)CNS coalition, DAML-S…) Environments for Creation of Ontologies (Protégé, Environments for Creation of Ontologies (Protégé,

Sandpiper, Construct, OilEd, …)Sandpiper, Construct, OilEd, …) Environments for Maintenance of Ontologies Environments for Maintenance of Ontologies

(Chimaera, OntoBuilder, …)(Chimaera, OntoBuilder, …) Reasoning Environments (Cerebra, Fact, JTP, Snark, Reasoning Environments (Cerebra, Fact, JTP, Snark,

…)…) Distributed explanation support (Inference Web)Distributed explanation support (Inference Web) Training (Conceptual Modeling, reasoning usage, Training (Conceptual Modeling, reasoning usage,

tutorials – OWL Guide, Ontologies 101, OWL tutorials – OWL Guide, Ontologies 101, OWL Tutorial, …)Tutorial, …)


Discussion/Discussion/ConclusionConclusion

• Ontologies are exploding; core of many applicationsOntologies are exploding; core of many applications• Business “pull” is driving ontology language tools and Business “pull” is driving ontology language tools and

languageslanguages• New generation applications need more expressive ontologies New generation applications need more expressive ontologies

and more back end reasoningand more back end reasoning• New generation users (the general public) need more support New generation users (the general public) need more support

than previous users of KR&R systemsthan previous users of KR&R systems• Distributed ontologies need more support: merging, analysis, Distributed ontologies need more support: merging, analysis,

explanation support, incompleteness techniques, versioning, explanation support, incompleteness techniques, versioning, etc.etc.

• Scale and distribution of the web force mind shiftScale and distribution of the web force mind shift• Everyone is in the game – US Government (DARPA, NSF, NIST, Everyone is in the game – US Government (DARPA, NSF, NIST,

ARDA…), EU, W3C, consortiums, business, …ARDA…), EU, W3C, consortiums, business, …• Consulting and product companies are in the space (not just Consulting and product companies are in the space (not just

academics)academics)This is THE time for ontology work!!!This is THE time for ontology work!!!


PointersPointersSelected 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/


ExtrasExtras


<rdfs:Class rdf:ID="BLAND-FISH-COURSE"> <daml:intersectionOf rdf:parseType="daml:collection"> <rdfs:Class rdf:about="#MEAL-COURSE"/> <daml:Restriction> <daml:onProperty rdf:resource="#FOOD"/> <daml:toClass rdf:resource="#BLAND-FISH"/> </daml:Restriction> </daml:intersectionOf> <rdfs:subClassOf rdf:resource="#DRINK-HAS-DELICATE-FLAVOR-

RESTRICTION"/> </rdfs:Class> <rdfs:Class rdf:ID="BLAND-FISH"> <rdfs:subClassOf rdf:resource="#FISH"/> <daml:disjointWith rdf:resource="#NON-BLAND-FISH"/> </rdfs:Class> <rdf:Description rdf:ID="FLOUNDER"> <rdf:type rdf:resource="#BLAND-FISH"/> </rdf:Description> <rdfs:Class rdf:ID="CHARDONNAY"> <rdfs:subClassOf rdf:resource="#WHITE-COLOR-RESTRICTION"/> <rdfs:subClassOf rdf:resource="#MEDIUM-OR-FULL-BODY-

RESTRICTION"/> <rdfs:subClassOf rdf:resource="#MODERATE-OR-STRONG-FLAVOR-

RESTRICTION"/> […] </rdfs:Class> <rdf:Description rdf:ID="BANCROFT-CHARDONNAY"> <rdf:type rdf:resource="#CHARDONNAY"/> <REGION rdf:resource="#NAPA"/> <MAKER rdf:resource="#BANCROFT"/> <SUGAR rdf:resource="#DRY"/> […] </rdf:Description>


DAML/OWL DAML/OWL Language Language

Web Languages

RDF/SXML

DAML-ONT

Formal FoundationsDescription Logics

FACT, CLASSIC, DLP, …

Frame Systems

DAML+OILOWL

OIL

•Extends vocabulary of XML and RDF/S•Rich ontology representation language•Language features chosen for efficient implementations


IssuesIssues Collaboration among distributed teamsCollaboration among distributed teams Interconnectivity with many systems/standardsInterconnectivity with many systems/standards Analysis and diagnosisAnalysis and diagnosis ScaleScale VersioningVersioning SecuritySecurity Ease of useEase of use Diverse training levels / user supportDiverse training levels / user support Presentation stylePresentation style LifecycleLifecycle ExtensibilityExtensibility


Services OntologiesServices Ontologies

DAML-S http://www.daml.org/services/DAML-S http://www.daml.org/services/ publication referencespublication references ontology specificationsontology specifications examplesexamples

A few interesting projects using DAML-S:A few interesting projects using DAML-S:

MyGrid: (http://mygrid.man.ac.uk)MyGrid: (http://mygrid.man.ac.uk) AgentCities (http://www.agentcities.org)AgentCities (http://www.agentcities.org) Services composer Services composer

(http://www.mindswap.org/~evren/composer/)(http://www.mindswap.org/~evren/composer/)


General Nature of General Nature of DescriptionsDescriptions

a 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


SUMOSUMO Available in KIF (first order logic), DAML, LOOM and Available in KIF (first order logic), DAML, LOOM and

XMLXML May be used without fee for any purpose (including for May be used without fee for any purpose (including for

profit)profit) Mapped by hand to 100,000 synsets of WordNet lexiconMapped by hand to 100,000 synsets of WordNet lexicon Validated with formal theorem provingValidated with formal theorem proving 52 publicly released versions created over two years 52 publicly released versions created over two years

(approximately 1,000 concepts, 4000 assertions, and 750 (approximately 1,000 concepts, 4000 assertions, and 750 rules so far)rules so far)

Specialized with dozens of free domain ontologiesSpecialized with dozens of free domain ontologies In use by companies, universities and government around In use by companies, universities and government around

the worldthe world Acadmica Sinica – Taiwan, U Arizona, lookwayup.com, NIST etcAcadmica Sinica – Taiwan, U Arizona, lookwayup.com, NIST etc

Available at http://ontology.teknowledge.comAvailable at http://ontology.teknowledge.com


Chimaera – A Chimaera – A Ontology Ontology

Environment ToolEnvironment ToolAn 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; used in HORUS to

support counter-terrorism ontology generation

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

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


Layer Cake Foundation Layer Cake Foundation




Some PointersSome Pointers

Ontologies Come of Age Paper: Ontologies Come of Age Paper: http://www.ksl.stanford.edu/people/dlm/pahttp://www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.htmlpers/ontologies-come-of-age-abstract.html

Ontologies and Online Commerce Paper: Ontologies and Online Commerce Paper: http://www.ksl.stanford.edu/people/dlm/pahttp://www.ksl.stanford.edu/people/dlm/papers/ontologies-and-online-commerce-abstrpers/ontologies-and-online-commerce-abstract.htmlact.html

DAML+OIL: DAML+OIL: http://www.daml.org/http://www.daml.org/ WEBONT: WEBONT:

http://www.w3.org/2001/sw/WebOnt/http://www.w3.org/2001/sw/WebOnt/ OWL: OWL: http://www.w3.org/TR/owl-features/http://www.w3.org/TR/owl-features/


E-Commerce Search E-Commerce Search (starting point Forrester Research modified by (starting point Forrester Research modified by

McGuinness)McGuinness) Ask QueriesAsk Queries - multiple search interfaces (surgical shoppers, advice seekers, window - multiple search interfaces (surgical shoppers, advice seekers, window

shoppers)shoppers) - set user expectations (interactive query refinement) - set user expectations (interactive query refinement) - anticipate anomalies- anticipate anomalies Get AnswersGet Answers - basic information (multiple sorts, filtering, structuring)- basic information (multiple sorts, filtering, structuring) - modify results (user defined parameters for refining, user profile info, - modify results (user defined parameters for refining, user profile info,

narrow query, broaden query, disambiguate query)narrow query, broaden query, disambiguate query) - suggest alternatives (suggest other comparable products even from - suggest alternatives (suggest other comparable products even from

competitor’s sites)competitor’s sites) Make DecisionsMake Decisions - manipulate results (enable side by side comparison)- manipulate results (enable side by side comparison) - dive deeper (provide additional info, multimedia, other views)- dive deeper (provide additional info, multimedia, other views) - take action (buy)- take action (buy)


The Need For KB The Need For KB AnalysisAnalysis

Large-scale knowledge repositories will necessarily contain KBs Large-scale knowledge repositories will necessarily contain KBs produced by multiple authors in multiple settings produced by multiple authors in multiple settings

KBs for applications will typically be built by assembling and KBs for applications will typically be built by assembling and extending multiple modular KBs from repositories extending multiple modular KBs from repositories that may not be that may not be consistentconsistent

KBs developed by multiple authors will frequentlyKBs developed by multiple authors will frequently Express overlapping knowledge in Express overlapping knowledge in different, possibly different, possibly

contradictory wayscontradictory ways Use differing Use differing assumptionsassumptions and and stylesstyles

For such KBs to be used as building blocks -For such KBs to be used as building blocks -

They must be reviewed for appropriateness and “correctness”They must be reviewed for appropriateness and “correctness” That is, they must be That is, they must be analyzedanalyzed


Our KB Analysis TaskOur KB Analysis Task

ReviewReview KBs that: KBs that: Were developed Were developed using differing standardsusing differing standards May be syntactically but not semantically validatedMay be syntactically but not semantically validated May use differing May use differing modelingmodeling representations representations

Produce KB logs (in interactive environments)Produce KB logs (in interactive environments) Identify provable problemsIdentify provable problems Suggest possible problems in style and/or modelingSuggest possible problems in style and/or modeling Are extensible by being user programmableAre extensible by being user programmable

Documents

Ontologies Come of Age Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford,