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Ontologies for Information Fusion

Ontologies for Information Fusion. Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford, CA 94305 USA 650-723-9770 dlm@ksl.stanford.edu. What is an Ontology?. General Description Logics*. Formal taxonomy.

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Ontologies for Information Fusion

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

  2. What is an Ontology? General Description Logics* Formal taxonomy Thesauri -> “narrower term” relation Frames (properties) Catalog/ ID Term Hierarchy (e.g. Yahoo!) Formal instance General Logic Terms/ glossary Value Restrs. *based on AAAI ’99 Ontologies panel – Gruninger, Lehmann, McGuinness, Uschold, Welty Updated by McGuinness, additional input from Gruninger, Uschold, and Rockmore Deborah L. McGuinness

  3. Some uses of (simple) Ontologies Simple 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) • Sense disambiguation (e.g., TAP) Deborah L. McGuinness

  4. Uses of Ontologies II • Interoperability Support • Consistency Checking • Completion • Support for validation and verification testing (e.g. http://ksl.stanford.edu/projects/DAML/chimaera-jtp-cardinality-test1.daml ) • Configuration support • Structured, “surgical” comparative customized search • Generalization / Specialization • Query and answer analysis and refinement See pedagogical wine agent example at: http://www.ksl.stanford.edu/people/dlm/webont/wineAgent/ Deborah L. McGuinness

  5. KSL Wine AgentSemantic Web Integration • Agent receives an analysis and retrieval task description and uses emerging web standards to provide answer description and return specific answers. (Given a meal description, describe the class(es) of matching wines and retrieve some from web.) • 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 information • Inference Web for explaining and validating answers (descriptions or instances) • [Web Services for interfacing with vendors] • Connections to online web agents/information services • Utilities for conducting and caching the above transactions • Info: http://www.ksl.stanford.edu/people/dlm/webont/wineAgent/ Deborah L. McGuinness

  6. Implications and Needs for Ontology-enhanced applications • Ontology language syntax and semantics (DAML+OIL, OWL) • Upper level/core ontologies for reuse/extension (Cyc, SUMO, CNS coalition, DAML-S…) • Environments for creation of ontologies (Protégé, Sandpiper, Construct, OilEd, …) • Environments for maintenance of ontologies: evolution, diagnostics, merging, partitioning, views, versions, (Chimaera, OntoBuilder, Prompt, …) • Reasoning environments (Cerebra, Fact, JTP, Snark, …) • Distributed explanation support facilitating trust (Inference Web) • Surrounding tools – semantic search (TAP, FindUR, …), agent platforms, • Training (conceptual modeling, reasoning usage, tutorials – OWL Guide, Ontologies 101, OWL Tutorial, …) Deborah L. McGuinness

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  9. Inference Web Infrastructure for trust. Supports explanation of reasoning and retrieval tasks by storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments • DAML+OIL/OWL specification of proofs is interlingua for proof interchange • Proof browser for displaying IW proofs and their explanations (possibly from multiple inference engines) • Registration for inference engines/rules/languages; pedigree • Proof explainer for abstracting proofs into more understandable formats • Proof generation service for facilitate the creation of IW proofs by inference engines • Hosted service available integrated with Stanford’s JTP reasoner and SRI’s SNARK reasoner. Integrated in DQL Client/Server, Wine Agent, … • Discussions with Boeing, Cycorp, Fetch, ISI, Northwestern, SRI, UT, UW, W3C, … Deborah L. McGuinness

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

  11. Discussion/Conclusion • Ontologies are exploding; core of many applications as seen at IF2003 • Business/govt. “pull” is driving ontology tools and languages • New generation applications need more expressive ontologies and more back end reasoning • User base is broader thus tools are providing support aimed at audience larger than KR&R-trained people • Distributed ontologies motivating more supporting tools: merging, analysis, explanation support, incompleteness techniques, versioning, etc. • Scale and distribution of the web force mind shift (no longer monolithic single ontologies) • Everyone is in the game – Government (DARPA, NSF, NIST, ARDA…), DSTO, EU, W3C, consortiums, business, … • Consulting and product companies are in the space (not just academics) Good time to bring ontologies into Info. Fusion in a larger way Deborah L. McGuinness

  12. A few US Govt. Programs • DARPA: • DAML – DARPA Agent Markup Language • RKF – Rapid Knowledge Formation • HPKB – High Performance Knowledge Base • PBA – Predictive Battle Space Awareness • EPCA – Enduring Personalized Cognitive Assistant/PAL/CALO, KnowledgePad • ARDA: • AQUAINT – Question Answering • NIMD – Novel Intelligence for Massive Data Deborah L. McGuinness

  13. Pointers • Selected 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/ Deborah L. McGuinness

  14. Extras Deborah L. McGuinness

  15. General Nature of Descriptions class a WINE superclass a LIQUID a POTABLE-THING 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 number/card restrictions structured components Roles/ properties value restrictions interconnections between parts Deborah L. McGuinness

  16. A Few Observations about Ontologies • Ontologies can be built by non-experts by COTS and academic tools • Verity’s Topic Editor, Constructor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-ED, etc. • Ontologies can be semi-automatically generated • from crawls of site such as yahoo!, amazon, excite, etc. • Semi-structured sites can provide starting points • Ontologies are exploding (business pull instead of technology push) • e-commerce - MySimon, Amazon, Yahoo! Shopping, VerticalNet, … • 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- Vulcan’s HALO project • Markup Languages growing XML,RDF, DAML, OWL,RuleML, xxML • “Real” ontologies are becoming more central to applications • Search companies moving towards them – Yahoo, recently Google Deborah L. McGuinness

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  18. Processing • Given a description of a meal, • Use 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 Theorem Prover to deduce answers (and proofs) • Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.) • Access relevant web sites (wine.com, wine commune, …) to access current information • Use DAML-S for markup and protocol* http://www.ksl.stanford.edu/projects/wine/explanation.html Deborah L. McGuinness

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  21. Querying multiple online sources Deborah L. McGuinness

  22. FindUR Architecture Content to Search: CLASSIC Knowledge Representation System 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 Content (Web Pages or Databases Content Classification Domain Knowledge Domain Knowledge Search Technology: Search Engine Verity (and topic sets) GUI supporting browsing and selection Collaborative Topic Set Tool User Interface: Verity SearchScript, Javascript, HTML, CGI, CLASSIC Results (standard format) Results (domain specific) Deborah L. McGuinness

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  27. <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> Deborah L. McGuinness

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

  29. Issues • Collaboration among distributed teams • Interconnectivity with many systems/standards • Analysis and diagnosis • Scale • Versioning • Security • Ease of use • Diverse training levels / user support • Presentation style • Lifecycle • Extensibility Deborah L. McGuinness

  30. Services Ontologies DAML-S http://www.daml.org/services/ • publication references • ontology specifications • examples A few interesting projects using DAML-S: • MyGrid: (http://mygrid.man.ac.uk) • AgentCities (http://www.agentcities.org) • Services composer (http://www.mindswap.org/~evren/composer/) Deborah L. McGuinness

  31. 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 Deborah L. McGuinness

  32. SUMO • Available in KIF (first order logic), DAML, LOOM and XML • May be used without fee for any purpose (including for profit) • Mapped by hand to 100,000 synsets of WordNet lexicon • Validated with formal theorem proving • 52 publicly released versions created over two years (approximately 1,000 concepts, 4000 assertions, and 750 rules so far) • Specialized with dozens of free domain ontologies • In use by companies, universities and government around the world • Acadmica Sinica – Taiwan, U Arizona, lookwayup.com, NIST etc • Available at http://ontology.teknowledge.com Deborah L. McGuinness

  33. 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/chimaera Deborah L. McGuinness

  34. Layer Cake Foundation Deborah L. McGuinness

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  37. Some Pointers • Ontologies Come of Age Paper: http://www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html • Ontologies and Online Commerce Paper: http://www.ksl.stanford.edu/people/dlm/papers/ontologies-and-online-commerce-abstract.html • DAML+OIL: http://www.daml.org/ • WEBONT: http://www.w3.org/2001/sw/WebOnt/ • OWL: http://www.w3.org/TR/owl-features/ Deborah L. McGuinness

  38. E-Commerce Search (starting point Forrester Research modified by McGuinness) • Ask Queries - multiple search interfaces (surgical shoppers, advice seekers, window shoppers) - set user expectations (interactive query refinement) - anticipate anomalies • Get Answers - basic information (multiple sorts, filtering, structuring) - modify results (user defined parameters for refining, user profile info, narrow query, broaden query, disambiguate query) - suggest alternatives (suggest other comparable products even from competitor’s sites) • Make Decisions - manipulate results (enable side by side comparison) - dive deeper (provide additional info, multimedia, other views) - take action (buy) Deborah L. McGuinness

  39. The Need For KB Analysis • 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 developed by multiple authors will frequently • Express overlapping knowledge in different, possibly contradictory ways • Use differing assumptions and styles • For such KBs to be used as building blocks - They must be reviewed for appropriateness and “correctness” • That is, they must be analyzed Deborah L. McGuinness

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

  41. A Few Observations about Ontologies • Ontologies can be built by non-experts by COTS and academic tools • Verity’s Topic Editor, Constructor, Collaborative Topic Builder, GFP, Chimaera, Protégé, OIL-ED, etc. • Ontologies can be semi-automatically generated • from crawls of site such as yahoo!, amazon, excite, etc. • Semi-structured sites can provide starting points • Ontologies are exploding (business pull instead of technology push) • e-commerce - MySimon, Amazon, Yahoo! Shopping, VerticalNet, … • 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- Vulcan’s HALO project • Markup Languages growing XML,RDF, DAML, OWL,RuleML, xxML • “Real” ontologies are becoming more central to applications • Search companies moving towards them – Yahoo, recently Google Deborah L. McGuinness

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