ontologies and the semantic web deborah l. mcguinness associate director and senior research...

Ontologies and the Ontologies and the Semantic WebSemantic Web

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

August 9, 2004 2Deborah L. McGuinness

OutlineOutline The Web is moving to a Semantic WebThe Web is moving to a Semantic Web

What is itWhat is it How can a web with semantics be usedHow can a web with semantics be used

Ontologies Ontologies What are theyWhat are they How can they be usedHow can they be used

Second SessionSecond Session How can I get started (a look at requirements, languages, ad How can I get started (a look at requirements, languages, ad

tools)tools) Discussion in an example domainDiscussion in an example domain

Session 1: Based loosely on Ontologies Come of Age.Session 1: Based loosely on Ontologies Come of Age.Session 2: Based loosely on Ontology Engineering 101, OWL Session 2: Based loosely on Ontology Engineering 101, OWL

Overview, and OWL Guide, How and When to Live with a Kl-Overview, and OWL Guide, How and When to Live with a Kl-ONE-like SystemONE-like System


Yesterday: Rich Information Yesterday: Rich Information Source for Human Source for Human

Manipulation/InterpretationManipulation/Interpretation

Human

Human

Human

Human


““I know what was input”I know what was input”

The web knows what text was The web knows what text was input input (and is (and is great at information dissemination) but great at information dissemination) but does little interpretation, manipulation, does little interpretation, manipulation, integration, and action. integration, and action.

Analogous to a new assistant who is Analogous to a new assistant who is thorough yet lacks common sense, thorough yet lacks common sense, context, adaptability, and the ability to context, adaptability, and the ability to interpret for youinterpret for you

Some people view this as the “syntactic Some people view this as the “syntactic web”web”


Moving to… Rich Information Moving to… Rich Information Source for Agent Source for Agent

Manipulation/InterpretationManipulation/Interpretation

Human

Agent

Agent


““I know what was meant”I know what was meant”

Understand term meaning and user Understand term meaning and user backgroundbackground

Interoperable (can translate between Interoperable (can translate between applications)applications)

Programmable (thus agent friendly and Programmable (thus agent friendly and operational)operational)

Explainable (thus maintains context and can Explainable (thus maintains context and can adapt)adapt)

Capable of filtering (thus limiting display and Capable of filtering (thus limiting display and human intervention requirements)human intervention requirements)

Capable of executing servicesCapable of executing services


Having a web that knows “what you want” or “what you Having a web that knows “what you want” or “what you mean” is accomplished by semantics…. specifically using mean” is accomplished by semantics…. specifically using semantic annotation on web resourcessemantic annotation on web resources

Scientific American, May 2001:


Semantic EnablersSemantic Enablers

Languages for representing term meaning Languages for representing term meaning – used to build ontologies– used to build ontologies

Tools for generating, maintaining, and Tools for generating, maintaining, and evolving ontologiesevolving ontologies

Tools for reasoning with and using Tools for reasoning with and using semantically enhanced applicationssemantically enhanced applications


Layer Cake Foundation Layer Cake Foundation


What is an What is an Ontology?Ontology?

Catalog/ID

GeneralLogical

constraints

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance

Value Restrs.

Disjointness, Inverse, part-

of…

*based on AAAI ’99 Ontologies panel – McGuinness, Welty, Ushold, Gruninger, Lehmann


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


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 and Navigation SupportSite Organization and Navigation Support Expectation setting (left side of many web pages)Expectation setting (left side of many web pages) ““Umbrella” Upper Level Structures (for extension)Umbrella” Upper Level Structures (for extension) Browsing support (tagged structures such as Browsing support (tagged structures such as

Yahoo!)Yahoo!) Search support (query expansion approaches such Search support (query expansion approaches such

as FindUR, e-Cyc)as FindUR, e-Cyc) Sense disambiguationSense disambiguation


Example Search Example Search ApplicationApplication

Research exemplar of many “smart” Research exemplar of many “smart” search applicationssearch applications


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. Configuration supporttesting (e.g. Configuration 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


<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>


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

Use OWL-QL/DQL to state a premise (the meal) and Use OWL-QL/DQL to state a premise (the meal) and query the knowledge base for a suggestion for a query the knowledge base for a suggestion for a wine description or set of instanceswine description or set of instances

Use JTP to deduce answers (and proofs)Use JTP to deduce answers (and 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, …) to access Access relevant web sites (wine.com, …) to access

current informationcurrent information Use OWL-S for markup and protocol*Use OWL-S for markup and protocol*

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


Querying multiple online Querying multiple online sourcessources


Observations from the Wine Observations from the Wine AgentAgent

Background knowledge is reasonably simple and built in Background knowledge is reasonably simple and built in OWL (includes foods and wine and pairing information OWL (includes foods and wine and pairing information similar to the OWL Guide, Ontology Engineering 101, similar to the OWL Guide, Ontology Engineering 101, CLASSIC Tutorial, …)CLASSIC Tutorial, …)

Background knowledge can be used for simple query Background knowledge can be used for simple query expansion over wine sources to retrieve for example expansion over wine sources to retrieve for example documents about red wines (including zinfandel, syrah, …)documents about red wines (including zinfandel, syrah, …)

Background knowledge used to interact with structured Background knowledge used to interact with structured queries such as those possible on wine.comqueries such as those possible on wine.com

Constraints allows a reasoner like JTP to infer Constraints allows a reasoner like JTP to infer consequences of the premises and query.consequences of the premises and query.

Explanation system (Inference Web) can provide Explanation system (Inference Web) can provide provenance information such as information on the provenance information such as information on the knowledge source (McGuinness’ wine ontology) and data knowledge source (McGuinness’ wine ontology) and data sources (such as wine.com)sources (such as wine.com)

Services work could allow automatic “matchmaking” Services work could allow automatic “matchmaking” instead of hand coded linkages with web resourcesinstead of hand coded linkages with web resources


Semantically Driven Semantically Driven Information Rich Task Information Rich Task

Architecture: KANI Architecture: KANI Relevant

KnowledgeIdentification

(TAP)

The World

Corpus

ExtractedKnowledge

DB WorkingKB

Selection

KnowledgeExtraction

Devil’sAdvocate

HypothesisModeling &

Testing

SemanticSearch

ExplanationGeneration

KeywordSearch

SharedReasoning

AnalysisManagement

BackgroundKB

Ontology

Entities

Models

KnowledgeTransfer

KnowledgeInteraction

KnowledgeBrowsing &Selection

Legend

Data

SystemComponent

SystemService

User InterfaceFeature

InferenceWeb


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 - Amazon, Yahoo! Shopping, VerticalNet, …e-commerce - 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,OWL,RuleML, xxMLMarkup Languages growing XML,RDF, DAML,OWL,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 (DAML+OIL, Ontology Language Syntax and Semantics (DAML+OIL,

OWL)OWL) Upper Level and Domain ontologies for reuse (Cyc, Upper Level and Domain ontologies for reuse (Cyc,

SUMO, CNS coalition, DAML-S… UMLS, GO, …)SUMO, CNS coalition, DAML-S… UMLS, GO, …) 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, …) Environment support for Explanation (Inference Web, Environment support for Explanation (Inference Web,

…)…) Training (Conceptual Modeling, reasoning usage, Training (Conceptual Modeling, reasoning usage,

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


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


W3C Web Ontology W3C Web Ontology Working Group and OWLWorking Group and OWL

WebOnt is part of W3C Semantic Web WebOnt is part of W3C Semantic Web Activity aimed at extending meta-data effortsActivity aimed at extending meta-data efforts

Begins from DAML+OIL W3C Note in 2001Begins from DAML+OIL W3C Note in 2001 Produces OWL which reached Produces OWL which reached

recommendation status in February 2004recommendation status in February 2004 OWL receives testimonials, news coverage, OWL receives testimonials, news coverage,

and usage escalatesand usage escalates Best Practices Working Group Best Practices Working Group Companies such as Network Inference, Companies such as Network Inference,

Sandpiper, etc support OWL as do open Sandpiper, etc support OWL as do open source and research orgssource and research orgs


visual ontology modelervisual ontology modeler™ ™ (VOM) 1.x(VOM) 1.x


CONSTRUCTCONSTRUCT**

Rapid Modeling, Visual EditingRapid Modeling, Visual Editing

Provides graphical and text environment for editing

Exports to OWL; Processed by Cerebra Server

* All Rights Reserved by Network Inference Inc


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


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


Inference Web Inference Web Framework for Framework for explainingexplaining question answering tasks by abstracting, question answering tasks by abstracting,

storing, exchanging, combining, annotating, filtering, storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof segmenting, comparing, and rendering proofs and proof fragments provided by question answerersfragments provided by question answerers

IW’s Proof Markup Language (PML)IW’s Proof Markup Language (PML) is an interlingua for is an interlingua for proof interchangeproof interchange

IWBaseIWBase is a distributed repository of meta-information related is a distributed repository of meta-information related to proofs and their explanationsto proofs and their explanations

IW BrowserIW Browser is an IW tool for displaying PML documents is an IW tool for displaying PML documents containing proofs and explanations (possibly from multiple containing proofs and explanations (possibly from multiple inference engines)inference engines)

IW ExplainerIW Explainer is an IW tool for abstracting proofs into more is an IW tool for abstracting proofs into more understandable formatsunderstandable formats


DiscussionDiscussion• The Semantic Web is arriving – annotation information is The Semantic Web is arriving – annotation information is

emerging – may be hand done or simple meta tags such emerging – may be hand done or simple meta tags such as date, author, etc.as date, author, etc.

• 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 New generation applications need more expressive

ontologies and more back end reasoningontologies and more back end reasoning• Everyone is in the game – US Government (DARPA, NSF, Everyone is in the game – US Government (DARPA, NSF,

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

just academics)just academics)

• This is THE time for ontology work…. This is THE time for ontology work….


Conclusion/NextConclusion/Next Languages are stable, endorsed, and Languages are stable, endorsed, and

available – e.g., OWL from W3Cavailable – e.g., OWL from W3C Tools are stable, although less Tools are stable, although less

standardized, available open source and standardized, available open source and commercially – e.g., Protégé, Sandpiper, commercially – e.g., Protégé, Sandpiper, Network Inference, …Network Inference, …

Next session will introduce how to get Next session will introduce how to get started identifying requirements, started identifying requirements, language overview, and tool support with language overview, and tool support with an examplean example


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


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

ontologies and the semantic web deborah l. mcguinness associate director and senior research...

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