1. Ontologies and the Semantic Web Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford, CA 94305 650-723-9770 dlm@ksl.stanford.edu dlm@ksl.stanford.edu

2. August 9, 2004 1Deborah L. McGuinness Outline The Web is moving to a Semantic Web The Web is moving to a Semantic Web What is it What is it How can a web with semantics be used How can a web with semantics be used Ontologies Ontologies What are they What are they How can they be used How can they be used Second Session Second Session How can I get started (a look at requirements, languages, ad tools) How can I get started (a look at requirements, languages, ad tools) Discussion in an example domain Discussion in an example domain Session 1: Based loosely on Ontologies Come of Age. Session 2: Based loosely on Ontology Engineering 101, OWL Overview, and OWL Guide, How and When to Live with a Kl-ONE-like System

3. August 9, 2004 2Deborah L. McGuinness Yesterday: Rich Information Source for Human Manipulation/Interpretation Human

4. August 9, 2004 3Deborah L. McGuinness “I know what was input” The web knows what text was input (and is great at information dissemination) but does little interpretation, manipulation, integration, and action. Analogous to a new assistant who is thorough yet lacks common sense, context, adaptability, and the ability to interpret for you Some people view this as the “syntactic web”

5. August 9, 2004 4Deborah L. McGuinness Moving to… Rich Information Source for Agent Manipulation/Interpretation Human Agent

6. August 9, 2004 5Deborah L. McGuinness “I know what was meant” Understand term meaning and user background Understand term meaning and user background Interoperable (can translate between applications) Interoperable (can translate between applications) Programmable (thus agent friendly and operational) Programmable (thus agent friendly and operational) Explainable (thus maintains context and can adapt) Explainable (thus maintains context and can adapt) Capable of filtering (thus limiting display and human intervention requirements) Capable of filtering (thus limiting display and human intervention requirements) Capable of executing services Capable of executing services

7. August 9, 2004 6Deborah L. McGuinness Having a web that knows “what you want” or “what you mean” is accomplished by semantics…. specifically using semantic annotation on web resources Scientific American, May 2001:

8. August 9, 2004 7Deborah L. McGuinness Semantic Enablers Languages for representing term meaning – used to build ontologies Languages for representing term meaning – used to build ontologies Tools for generating, maintaining, and evolving ontologies Tools for generating, maintaining, and evolving ontologies Tools for reasoning with and using semantically enhanced applications Tools for reasoning with and using semantically enhanced applications

9. August 9, 2004 8Deborah L. McGuinness Layer Cake Foundation

10. August 9, 2004 9Deborah L. McGuinness What is an Ontology? Catalog/ ID General Logical constraints Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs. Disjointness, Inverse, part- of… *based on AAAI ’99 Ontologies panel – McGuinness, Welty, Ushold, Gruninger, Lehmann

11. August 9, 2004 10Deborah L. McGuinness General Nature of Descriptions a WINE a LIQUID a POTABLE grape: chardonnay,... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY grape dictates color (modulo skin) harvest time and sugar are related general categories structured components interconnections between parts

12. August 9, 2004 11Deborah L. McGuinness General Nature of Descriptions a WINE a LIQUID a POTABLE grape: chardonnay,... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY grape dictates color (modulo skin) harvest time and sugar are related general categories structured components interconnections between parts number/card restrictions value restrictions class superclass Roles/ properties

13. August 9, 2004 12Deborah L. McGuinness Some uses of Ontologies Simple ontologies (taxonomies) provide: Controlled shared vocabulary (search engines, authors, users, databases, programs/agents all speak same language) Controlled shared vocabulary (search engines, authors, users, databases, programs/agents all speak same language) Site Organization and Navigation Support Site 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 Yahoo!) Browsing support (tagged structures such as Yahoo!) Search support (query expansion approaches such as FindUR, e-Cyc) Search support (query expansion approaches such as FindUR, e-Cyc) Sense disambiguation Sense disambiguation

14. August 9, 2004 13Deborah L. McGuinness Example Search Application Research exemplar of many “smart” search applications

15. August 9, 2004 14Deborah L. McGuinness FindUR Architecture Search Engine Content to Search: Search Technology: User Interface: Verity (and topic sets) Content (Web Pages 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 Site Technical Memorandum Calendars (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

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20. August 9, 2004 19Deborah L. McGuinness Uses of Ontologies II Consistency Checking Consistency Checking Completion Completion Interoperability Support Interoperability Support Support for validation and verification testing (e.g. Configuration support Support for validation and verification testing (e.g. Configuration support Structured, “surgical” comparative customized search Structured, “surgical” comparative customized search Generalization / Specialization Generalization / Specialization … Foundation for expansion and leverage … Foundation for expansion and leverage

21. August 9, 2004 20Deborah L. McGuinness KSL Wine Agent Semantic 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

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24. August 9, 2004 23Deborah L. McGuinness Processing Given a description of a meal, Given a description of a meal, Use OWL-QL/DQL to state a premise (the meal) and query the knowledge base for a suggestion for a wine description or set of instances Use OWL-QL/DQL to state a premise (the meal) and query the knowledge base for a suggestion for a wine 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, instances, provenance, reasoning engines, etc.) Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.) Access relevant web sites (wine.com, …) to access current information Access relevant web sites (wine.com, …) to access current information Use OWL-S for markup and protocol* Use OWL-S for markup and protocol* http://www.ksl.stanford.edu/projects/wine/explanation.html

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27. August 9, 2004 26Deborah L. McGuinness Querying multiple online sources

28. August 9, 2004 27Deborah L. McGuinness Observations from the Wine Agent Background knowledge is reasonably simple and built in OWL (includes foods and wine and pairing information similar to the OWL Guide, Ontology Engineering 101, CLASSIC Tutorial, …) Background knowledge is reasonably simple and built in OWL (includes foods and wine and pairing information similar to the OWL Guide, Ontology Engineering 101, CLASSIC Tutorial, …) Background knowledge can be used for simple query expansion over wine sources to retrieve for example documents about red wines (including zinfandel, syrah, …) Background knowledge can be used for simple query expansion over wine sources to retrieve for example documents about red wines (including zinfandel, syrah, …) Background knowledge used to interact with structured queries such as those possible on wine.com Background knowledge used to interact with structured queries such as those possible on wine.com Constraints allows a reasoner like JTP to infer consequences of the premises and query. Constraints allows a reasoner like JTP to infer consequences of the premises and query. Explanation system (Inference Web) can provide provenance information such as information on the knowledge source (McGuinness’ wine ontology) and data sources (such as wine.com) Explanation system (Inference Web) can provide provenance information such as information on the knowledge source (McGuinness’ wine ontology) and data sources (such as wine.com) Services work could allow automatic “matchmaking” instead of hand coded linkages with web resources Services work could allow automatic “matchmaking” instead of hand coded linkages with web resources

29. August 9, 2004 28Deborah L. McGuinness Semantically Driven Information Rich Task Architecture: KANI

30. August 9, 2004 29Deborah L. McGuinness A Few Observations about Ontologies Simple ontologies can be built by non-experts Simple ontologies can be built by non-experts Verity’s Topic Editor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-ED, etc. Verity’s Topic Editor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-ED, etc. Ontologies can be semi-automatically generated Ontologies 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 points Semi-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, Open Directory (DMOZ), Rosetta Net, SUMO Controlled vocabularies (for the web) abound - SIC codes, UMLS, UNSPSC, Open Directory (DMOZ), Rosetta Net, SUMO Business interest expanding – ontology directors, business ontologies are becoming more complicated (roles, value restrictions, …), VC firms interested, Business interest expanding – ontology directors, business ontologies are becoming more complicated (roles, value restrictions, …), VC firms interested, Markup Languages growing XML,RDF, DAML,OWL,RuleML, xxML Markup Languages growing XML,RDF, DAML,OWL,RuleML, xxML “Real” ontologies are becoming more central to applications “Real” ontologies are becoming more central to applications Search companies moving towards them – Yahoo, recently Google Search companies moving towards them – Yahoo, recently Google

31. August 9, 2004 30Deborah L. McGuinness Implications and Needs for Ontology-enhanced applications Ontology Language Syntax and Semantics (DAML+OIL, OWL) Ontology Language Syntax and Semantics (DAML+OIL, OWL) Upper Level and Domain ontologies for reuse (Cyc, SUMO, CNS coalition, DAML-S… UMLS, GO, …) Upper Level and Domain ontologies for reuse (Cyc, SUMO, CNS coalition, DAML-S… UMLS, GO, …) Environments for Creation of Ontologies (Protégé, Sandpiper, Construct, OilEd, …) Environments for Creation of Ontologies (Protégé, Sandpiper, Construct, OilEd, …) Environments for Maintenance of Ontologies (Chimaera, OntoBuilder, …) Environments for Maintenance of Ontologies (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, tutorials – OWL Guide, Ontologies 101, OWL Tutorial, …) Training (Conceptual Modeling, reasoning usage, tutorials – OWL Guide, Ontologies 101, OWL Tutorial, …)

32. August 9, 2004 31Deborah L. McGuinness DAML/OWL Language Web Languages RDF/S XML DAML-ONT Formal Foundations Description Logics FACT, CLASSIC, DLP, … Frame Systems DAML+OIL OWL OIL Extends vocabulary of XML and RDF/S Rich ontology representation language Language features chosen for efficient implementations

33. August 9, 2004 32Deborah L. McGuinness W3C Web Ontology Working Group and OWL WebOnt is part of W3C Semantic Web Activity aimed at extending meta-data efforts WebOnt is part of W3C Semantic Web Activity aimed at extending meta-data effortsW3C Semantic Web Activity W3C Semantic Web Activity Begins from DAML+OIL W3C Note in 2001 Begins from DAML+OIL W3C Note in 2001DAML+OIL W3C Note DAML+OIL W3C Note Produces OWL which reached recommendation status in February 2004 Produces OWL which reached recommendation status in February 2004OWL OWL receives testimonials, news coverage, and usage escalates OWL receives testimonials, news coverage, and usage escalatestestimonialsnews coveragetestimonialsnews coverage Best Practices Working Group Best Practices Working Group Best Practices Working Group Best Practices Working Group Companies such as Network Inference, Sandpiper, etc support OWL as do open source and research orgs Companies such as Network Inference, Sandpiper, etc support OWL as do open source and research orgs

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36. August 9, 2004 35Deborah L. McGuinness visual ontology modeler™ (VOM) 1.x

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38. August 9, 2004 37Deborah L. McGuinness CONSTRUCT * Rapid Modeling, Visual Editing Provides graphical and text environment for editing Exports to OWL; Processed by Cerebra Server * All Rights Reserved by Network Inference Inc

39. August 9, 2004 38Deborah L. McGuinness Chimaera – A 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; 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/chimaerawww.ksl.stanford.edu/software/chimaera

40. August 9, 2004 39Deborah L. McGuinness The Need For KB Analysis Large-scale knowledge repositories will necessarily contain KBs produced by multiple authors in multiple settings Large-scale knowledge repositories will necessarily contain KBs produced by multiple authors in multiple settings KBs for applications will typically be built by assembling and extending multiple modular KBs from repositories that may not be consistent KBs for applications will typically be built by assembling and extending multiple modular KBs from repositories that may not be consistent KBs developed by multiple authors will frequently KBs developed by multiple authors will frequently Express overlapping knowledge in different, possibly contradictory ways Express overlapping knowledge in different, possibly contradictory ways Use differing assumptions and styles Use differing assumptions and styles 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” That is, they must be analyzed That is, they must be analyzed

41. August 9, 2004 40Deborah L. McGuinness Our KB Analysis Task Review KBs that: Review KBs that: Were developed using differing standards Were developed using differing standards May be syntactically but not semantically validated May be syntactically but not semantically validated May use differing modeling representations May use differing modeling representations Produce KB logs (in interactive environments) Produce KB logs (in interactive environments) Identify provable problems Identify provable problems Suggest possible problems in style and/or modeling Suggest possible problems in style and/or modeling Are extensible by being user programmable Are extensible by being user programmable

45. August 9, 2004 44Deborah L. McGuinness Inference Web Framework for explaining question answering tasks by abstracting, storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments provided by question answerers IW’s Proof Markup Language (PML) is an interlingua for proof interchange IW’s Proof Markup Language (PML) is an interlingua for proof interchange IWBase is a distributed repository of meta-information related to proofs and their explanations IWBase is a distributed repository of meta-information related to proofs and their explanations IW Browser is an IW tool for displaying PML documents containing proofs and explanations (possibly from multiple inference engines) IW Browser is an IW tool for displaying PML documents containing proofs and explanations (possibly from multiple inference engines) IW Explainer is an IW tool for abstracting proofs into more understandable formats IW Explainer is an IW tool for abstracting proofs into more understandable formats

46. August 9, 2004 45Deborah L. McGuinness Discussion The Semantic Web is arriving – annotation information is emerging – may be hand done or simple meta tags such as date, author, etc.The Semantic Web is arriving – annotation information is emerging – may be hand done or simple meta tags such 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 languagesBusiness “pull” is driving ontology language tools and languages New generation applications need more expressive ontologies and more back end reasoningNew generation applications need more expressive ontologies and more back end reasoning Everyone is in the game – US Government (DARPA, NSF, NIST, ARDA…), EU, W3C, consortiums, business, …Everyone is in the game – US Government (DARPA, NSF, NIST, ARDA…), EU, W3C, consortiums, business, … Consulting and product companies are in the space (not just academics)Consulting and product companies are in the space (not just academics) This is THE time for ontology work….This is THE time for ontology work….

47. August 9, 2004 46Deborah L. McGuinness Conclusion/Next Languages are stable, endorsed, and available – e.g., OWL from W3C Languages are stable, endorsed, and available – e.g., OWL from W3C Tools are stable, although less standardized, available open source and commercially – e.g., Protégé, Sandpiper, Network Inference, … Tools are stable, although less standardized, available open source and commercially – e.g., Protégé, Sandpiper, Network Inference, … Next session will introduce how to get started identifying requirements, language overview, and tool support with an example Next session will introduce how to get started identifying requirements, language overview, and tool support with an example

48. August 9, 2004 47Deborah L. McGuinnessPointers Selected Papers: - McGuinness. Ontologies come of age, 2003Ontologies come of age - Das, Wei, McGuinness, Industrial Strength Ontology Evolution Environments, 2002.Industrial Strength Ontology Evolution Environments - Kendall, Dutra, McGuinness. Towards a Commercial Strength Ontology Development Environment, 2002.Towards a Commercial Strength Ontology Development Environment - McGuinness Description Logics Emerge from Ivory Towers, 2001.Description Logics Emerge from Ivory Towers - McGuinness. Ontologies and Online Commerce, 2001.Ontologies and Online Commerce - McGuinness. Conceptual Modeling for Distributed Ontology Environments, 2000.Conceptual Modeling for Distributed Ontology Environments - McGuinness, Fikes, Rice, Wilder. An Environment for Merging and Testing Large Ontologies, 2000.An Environment for Merging and Testing Large Ontologies - Brachman, Borgida, McGuinness, Patel-Schneider. Knowledge Representation meets Reality, 1999.Knowledge Representation meets Reality - McGuinness. Ontological Issues for Knowledge-Enhanced Search, 1998.Ontological Issues for Knowledge-Enhanced Search - McGuinness and Wright. Conceptual Modeling for Configuration, 1998. Selected Tutorials: -Smith, Welty, McGuinness. OWL Web Ontology Language Guide, 2003.OWL Web Ontology Language Guide -Noy, McGuinness. Ontology Development 101: A Guide to Creating your First Ontology. 2001.Ontology Development 101: A Guide to Creating your First Ontology -Brachman, McGuinness, Resnick, Borgida. How and When to Use a KL-ONE-like System, 1991.How and When to Use a KL-ONE-like System Languages, Environments, Software: - OWL - http://www.w3.org/TR/owl-features/, http://www.w3.org/TR/owl-guide/http://www.w3.org/TR/owl-features/http://www.w3.org/TR/owl-guide/ - DAML+OIL: http://www.daml.org/http://www.daml.org/ - Inference Web - http://www.ksl.stanford.edu/software/iw/http://www.ksl.stanford.edu/software/iw/ - Chimaera - http://www.ksl.stanford.edu/software/chimaera/http://www.ksl.stanford.edu/software/chimaera/ - FindUR - http://www.research.att.com/people/~dlm/findur/http://www.research.att.com/people/~dlm/findur/ - TAP – http://tap.stanford.edu/http://tap.stanford.edu/ - DQL - http://www.ksl.stanford.edu/projects/dql/http://www.ksl.stanford.edu/projects/dql/

49. August 9, 2004 48Deborah L. McGuinness Extras

50. August 9, 2004 49Deborah L. McGuinnessIssues Collaboration among distributed teams Collaboration among distributed teams Interconnectivity with many systems/standards Interconnectivity with many systems/standards Analysis and diagnosis Analysis and diagnosis Scale Scale Versioning Versioning Security Security Ease of use Ease of use Diverse training levels / user support Diverse training levels / user support Presentation style Presentation style Lifecycle Lifecycle Extensibility Extensibility