1. Ontology-enhanced Search for Primary Care Literature 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.edudlm@ksl.stanford.edu (work supported by AT&T Labs Research, Florham Park, NJ in conjunction with NIST)

2. Outline u Background and Motivation u (Simple) Medical Applications u Collaborative Ontology Maintenance Environment u Discussion

3. Background u Description Logics u Co-author of widely used DL - CLASSIC u Knowledge Sharing Committee producing KRSS u Co-editor of forthcoming DL book u Conceptual Modeling u Co-organizer DL2000 (attended and/or org since ’84) u Research u Making KR&R systems usable (explanation, markup languages, expressiveness and/or functionality extensions – part-of, epistemic, …) u Collaborative ontology environments (merging, diagnostics, annotating, difference, focus of attention, libraries) u Applications u Configuration u Online services (electronic yellow pages, online calendars, Healthsite, Hometown…) u E-commerce u Medicine

4. FindUR (McGuinness, et. al.-WWW6 ’97, McGuinness-FOIS ’98) u Ontology-enhanced online search u Motivated by AT&T Personal Online Services needs of “friendlier and smarter” support for browsing and search u Exploits background knowledge and structured (or semi-structured) sites to provide query expansion in limited contexts u Applications: yellow pages, online calendars, competitive intelligence, Worldnet homepages, TM search, customer care, medical applications,... Collaborators: Lori Alperin Resnick, Tom Beattie, Harley Manning, Steve Solomon, Harry Moore

5. FindUR Architecture Search Engine Content to Search: Search and Representation Technology: User Interface: Verity Topic Sets Content (Web Pages, Documents, Databases) Results (domain spec.) Verity SearchScript, Javascript, HTML, CGI Content Classification Domain Knowledge Results (std. format) Search Parameters Classic Collaborative Topic Building Tool Query Input P-CHIP Research Site Technical Memorandum Calendars (Summit 2005, Research) Yellow Pages (Directory Westfield) Newspapers (Leader) AT&T Solutions Worldnet Customer Care

6. P-CHIP –Primary Care Health Information Provider - Russ Maulitz, Ihung Kyle Chang, Wes Hutchison, Eric Vogel, Bob Grealish, Nick DiCianni, George Garcia, Chris Sparks, Sudip Ghatak, … Vision: Ubiquitous access to ever-changing documents Online documents Partially marked up data (“pearl”, author, date,…) Initial user- docs; other users: health care workers; health care students; patients in waiting rooms,

7. Traits - Documents may not contain exact terms in queries (causing low recall) - Sites may contain exploitable structure - Vocabularies may vary - Users may benefit from help forming queries - Users may require varying granularity - Search within contexts

15. Discussion u Simple ontologies enhanced search and browsing experience u Mark-up and structure can be exploited u Critically dependent on ontologies (and their maintenance) u Ontology environments (for naïve and advanced users) u Validation u (Semi)-automatic input u Merging u Mark-up and structure can be exploited u Expressive markup languages u Automatic markup support u Markup validation tools

16. What is different now? u Size u Speed u Ontology “pull” in the market place u Tools for semi-automatic ontology generation and import u Tools for automatic markup generation u Availability of marked up data u Commercial search support u Research on ontology environments

17. Pointers u FindUR: www.research.att.com/~dlm/findurwww.research.att.com/~dlm/findur u CLASSIC: www.research.att.com/sw/tools/classicwww.research.att.com/sw/tools/classic u Chimaera: www.ksl.svc.stanford.edu:5915/doc/people/rice/chimaera/ chimaera-movie.avi www.ksl.svc.stanford.edu:5915/doc/people/rice/chimaera/ chimaera-movie.avi u Deborah McGuinness: www.ksl.stanford.edu/people/dlmwww.ksl.stanford.edu/people/dlm

18. Extra Slides

20. Acknowledgements PCHIP: Russ Maulitz Ihung (Kyle) Chang Eric Vogel, Bob Grealish Wes Hutchison Nick DiCianni George Garcia Chris Sparks Sudip Ghatak FindUR: Lori Alperin Resnick Tom Beattie Harley Manning Steve Solomon Mark Plotnick Dave Kormann

23. Applications u P-CHIP u Business Directories (Directory Westfield) u Telephone Listings (Directory Westfield, Rainbow Pages (predecessor to anywho.com)) u Project Information Resource (Research) u Public Events & News(Summit 2005, Westfield Calendar, Westfield Leader, AT&T Research) u AT&T Solutions Vendor Management u Network Service Realization Process Support u AT&T Labs Industry Relations Site u Technical Memorandum Database

24. FindUR: Advantages

25. FindUR Benefits u Retrieves documents otherwise missed u More appropriately organizes documents according to relevance (useful for large number of retrievals) u Browsing support (navigation, highlighting) u Simple User Query building and refinement u Full Query Logging and Trace u Facilitate use of advanced search functions without requiring knowledge of a search language u Automatically search the right knowledge sources according to information about the context of the query

26. Future Work u Topic Set Generation u Distributed Collaborative Topic Set Building Environment u Use tagged content to generate candidate topic sets u Information Retrieval (use clustering to analyze documents and suggest topic definitions) u Machine Learning (use query logs as training data) u Reuse topic sets for different purposes using views of knowledge u Knowledge Representation Integration u Use knowledge base to check definitions and determine overlaps u Expand beyond subclass, instance, and synonym relationships and incorporate more structured information u Maintain information about how and when to use topic information u Maintain descriptions of content sources u Evaluation and Interface Evolution u Evaluate on effectiveness of retrievals, relevance ranking, ease of query refinement, east of content input into category scheme u Java-based interface for scalability, rapid changing, understandability

28. 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…

29. Selected Experiences Online Configurators: PROSE/QUESTAR family of configurator applications for AT&T and LucentOnline Configurators: PROSE/QUESTAR family of configurator applications for AT&T and Lucent Data Mining applications for AT&T and NCRData Mining applications for AT&T and NCR Knowledge-enhanced web search – FindUR application family: electronic yellow pages, online calendars, competitive intelligence, staffing,Knowledge-enhanced web search – FindUR application family: electronic yellow pages, online calendars, competitive intelligence, staffing, Ontology mgmt applications and environments - Chimaera, Collaborative Topic builder,e-commerce ontologies,...Ontology mgmt applications and environments - Chimaera, Collaborative Topic builder,e-commerce ontologies,... Government ontology efforts: HPKB, intrusion detection, RKF, ArmyGovernment ontology efforts: HPKB, intrusion detection, RKF, Army Commercial Search - Cisco, WorldnetCommercial Search - Cisco, Worldnet KR&R Researcher: Description Logics, co-Author of CLASSIC, explanation of reasoning, meta languages for pruning, usability issues, ontology environmentsKR&R Researcher: Description Logics, co-Author of CLASSIC, explanation of reasoning, meta languages for pruning, usability issues, ontology environments Executive council for AAAI, Board of ontology.org, Board of Adsura.comExecutive council for AAAI, Board of ontology.org, Board of Adsura.com

30. Ontologies - extra u Simple Ontologies can be built by non-experts u Consider Verity’s Topic Editor, Collaborative Topic Builder, GFP interface, Chimaera, etc. u Ontologies can be semi-automatically generated u from crawls of site such as yahoo!, amazon, excite, etc. u Semi-structured sites can provide starting points u Ontologies are exploding (business pull instead of technology push) u most e-commerce sites are using them - MySimon, Affinia, Amazon, Yahoo! Shopping,, etc. u Controlled vocabularies (for the web) abound - SIC codes, UMLS, UN/SPSC, Open Directory, Rosetta Net, u DTDs and ontologies are a natural pairing to facilitate automatic extraction u KM applications require them

31. Other Topics of Interest u Description Logics u Ontology Libraries u Ontology Tools - Merging, pruning, explanation, etc. u Representation and Reasoning applications – configuration, completing records, customer care, etc.

32. Ontologies and importance to E-Commerce Simple ontologies provide: u Controlled shared vocabulary u Organization (and navigation support) u Expectation setting (left side of many web pages) u Browsing support (tagged structures such as Yahoo!) u Search support (query expansion approaches such as FindUR, e-Cyc) u Sense disambiguation

33. Ontologies and importance to E-Commerce II u Foundation for expansion and leverage u Conflict detection u Completion u Regression testing/validation/verification support foundation u Configuration support u Structured, comparative search u Generalization/ Specialization u …

34. E-Commerce Search (starting point Forrester modified by McGuinness) u Ask Queries - multiple search interfaces (surgical shoppers, advice seekers, window shoppers) - set user expectations (interactive query refinement, - anticipate anomalies u 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 u Make Decisions - manipulate results (enable side by side comparison) - dive deeper (provide additional info, multimedia, other views) - take action (buy)