Ontology-enhanced Search for Ontology-enhanced Search for Primary Care LiteraturePrimary Care Literature

Deborah L. McGuinnessDeborah L. McGuinnessAssociate Director and Senior Research ScientistAssociate Director and Senior Research Scientist

Knowledge Systems LaboratoryKnowledge Systems LaboratoryStanford UniversityStanford UniversityStanford, CA 94305Stanford, CA 94305

650-723-9770650-723-9770 dlm@ksl.stanford.edu

(work supported by AT&T Labs Research, Florham Park, NJ in conjunction with NIST)(work supported by AT&T Labs Research, Florham Park, NJ in conjunction with NIST)

OutlineOutline

Background and Motivation (Simple) Medical Applications Collaborative Ontology Maintenance

Environment Discussion

BackgroundBackground Description Logics

Co-author of widely used DL - CLASSIC Knowledge Sharing Committee producing KRSS Co-editor of forthcoming DL book Conceptual Modeling Co-organizer DL2000 (attended and/or org since ’84)

Research Making KR&R systems usable (explanation, markup languages, expressiveness and/or

functionality extensions – part-of, epistemic, …) Collaborative ontology environments (merging, diagnostics, annotating, difference, focus of

attention, libraries) Applications

Configuration Online services (electronic yellow pages, online calendars, Healthsite, Hometown…) E-commerce Medicine

FindUR FindUR (McGuinness, et. al.-WWW6 ’97, McGuinness-FOIS ’98)(McGuinness, et. al.-WWW6 ’97, McGuinness-FOIS ’98)

Ontology-enhanced online search Motivated by AT&T Personal Online Services needs of

“friendlier and smarter” support for browsing and search Exploits background knowledge and structured (or semi-

structured) sites to provide query expansion in limited contexts

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

FindUR Architecture

SearchEngine

Content to Search:

Search and Representation Technology:

User Interface:

Verity Topic Sets

Content (WebPages, Documents,

Databases)

Results(domain spec.)

Verity SearchScript, Javascript, HTML, CGI

Content

Classification

Domain

Knowledge

Results(std. format)

SearchParameters

Classic Collaborative Topic Building

ToolQuery Input

P-CHIPResearch SiteTechnical MemorandumCalendars (Summit 2005, Research) Yellow Pages (Directory Westfield)Newspapers (Leader) AT&T SolutionsWorldnet Customer Care

P-CHIP –Primary Care Health Information ProviderP-CHIP –Primary Care Health Information Provider - - Russ Maulitz, Ihung Kyle Chang, Wes Hutchison, Eric Vogel, Bob Grealish, Russ Maulitz, Ihung Kyle Chang, Wes Hutchison, Eric Vogel, Bob Grealish,

Nick DiCianni, George Garcia, Chris Sparks, Sudip Ghatak, …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,

TraitsTraits

- 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

DiscussionDiscussion Simple ontologies enhanced search and browsing experience Mark-up and structure can be exploited Critically dependent on ontologies (and their maintenance)

Ontology environments (for naïve and advanced users) Validation (Semi)-automatic input Merging

Mark-up and structure can be exploited Expressive markup languages Automatic markup support Markup validation tools

What is different now?What is different now?

Size Speed Ontology “pull” in the market place Tools for semi-automatic ontology generation and

import Tools for automatic markup generation Availability of marked up data Commercial search support Research on ontology environments

PointersPointers

FindUR: www.research.att.com/~dlm/findur CLASSIC: www.research.att.com/sw/tools/classic Chimaera:

www.ksl.svc.stanford.edu:5915/doc/people/rice/chimaera/chimaera-movie.avi

Deborah McGuinness: www.ksl.stanford.edu/people/dlm

Extra SlidesExtra Slides

AcknowledgementsAcknowledgements

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

ApplicationsApplications

P-CHIP Business Directories (Directory Westfield) Telephone Listings (Directory Westfield, Rainbow

Pages (predecessor to anywho.com)) Project Information Resource (Research) Public Events & News(Summit 2005, Westfield

Calendar, Westfield Leader, AT&T Research) AT&T Solutions Vendor Management Network Service Realization Process Support AT&T Labs Industry Relations Site Technical Memorandum Database

FindUR: AdvantagesFindUR: Advantages

Challenge Non-Enhanced Search Enhanced withDomain Knowledge

Access Large Amount ofInformation Easily

Publish Content onIntranet

Provide an Intuitive UI toeasily find usefulinformation

Quick Access toAvailable Information

Hours of Surfing -Many Retrievals to SiftThrough

1. Finds All RelevantMatches2. Lists Most RelevantMatches First

Facilitate Searching forNovice Users

Users Need to KnowSearch Terms

Relevant Terms are Pre-Defined

Create a “LearningOrganization”

No way to easily sharedomain knowledge

Provides CollaborationEnvironment for TopicBuilding

Make IterativeImprovements to Speedof Finding RelevantInformation

No Visibility to ActualQueries

Incorporates QueryLogging for MachineLearning and UIRefinement

FindUR BenefitsFindUR Benefits Retrieves documents otherwise missed More appropriately organizes documents

according to relevance (useful for large number of retrievals)

Browsing support (navigation, highlighting) Simple User Query building and refinement Full Query Logging and Trace Facilitate use of advanced search functions

without requiring knowledge of a search language Automatically search the right knowledge sources

according to information about the context of the query

Future WorkFuture Work Topic Set Generation

Distributed Collaborative Topic Set Building Environment Use tagged content to generate candidate topic sets Information Retrieval (use clustering to analyze documents and suggest topic definitions) Machine Learning (use query logs as training data) Reuse topic sets for different purposes using views of knowledge

Knowledge Representation Integration Use knowledge base to check definitions and determine overlaps Expand beyond subclass, instance, and synonym relationships and incorporate more structured

information Maintain information about how and when to use topic information Maintain descriptions of content sources

Evaluation and Interface Evolution Evaluate on effectiveness of retrievals, relevance ranking, ease of query refinement, east of content

input into category scheme Java-based interface for scalability, rapid changing, understandability

What is an Ontology?What is an Ontology?

Catalog/ID

GeneralLogical

constraints

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance

Value Restrs.

Disjointness, Inverse, part-

of…

Selected ExperiencesSelected Experiences• Online Configurators:Online Configurators: PROSE/QUESTAR family of configurator PROSE/QUESTAR family of configurator

applications for AT&T and Lucentapplications for AT&T and Lucent• Data MiningData Mining applications for AT&T and NCR applications for AT&T and NCR• Knowledge-enhanced web searchKnowledge-enhanced web search – FindUR application family: – FindUR application family:

electronic yellow pages, online calendars, competitive intelligence, electronic yellow pages, online calendars, competitive intelligence, staffing, staffing,

• Ontology mgmt applications and environmentsOntology mgmt applications and environments - Chimaera, - Chimaera, Collaborative Topic builder,e-commerce ontologies, ...Collaborative Topic builder,e-commerce ontologies, ...

• Government ontologyGovernment ontology efforts: HPKB, intrusion detection, RKF, Army efforts: HPKB, intrusion detection, RKF, Army• Commercial SearchCommercial Search - Cisco, Worldnet - Cisco, Worldnet• KR&R ResearcherKR&R Researcher: Description Logics, co-Author of CLASSIC, : Description Logics, co-Author of CLASSIC,

explanation of reasoning, meta languages for pruning, usability issues, explanation of reasoning, meta languages for pruning, usability issues, ontology environmentsontology environments

• Executive council for AAAI, Board of ontology.org, Board of Executive council for AAAI, Board of ontology.org, Board of Adsura.comAdsura.com

Ontologies - extraOntologies - extra Simple Ontologies can be built by non-experts

Consider Verity’s Topic Editor, Collaborative Topic Builder, GFP interface, Chimaera, 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) most e-commerce sites are using them - MySimon, Affinia, Amazon, Yahoo!

Shopping,, etc. Controlled vocabularies (for the web) abound - SIC codes, UMLS,

UN/SPSC, Open Directory, Rosetta Net, DTDs and ontologies are a natural pairing to facilitate automatic extraction KM applications require them

Other Topics of InterestOther Topics of Interest

Description Logics Ontology Libraries Ontology Tools - Merging, pruning,

explanation, etc. Representation and Reasoning applications

– configuration, completing records, customer care, etc.

Ontologies and importance to Ontologies and importance to E-CommerceE-Commerce

Simple ontologies provide: Controlled shared vocabulary Organization (and navigation support) Expectation setting (left side of many web pages) Browsing support (tagged structures such as Yahoo!) Search support (query expansion approaches such as

FindUR, e-Cyc) Sense disambiguation

Ontologies and importance to Ontologies and importance to E-Commerce IIE-Commerce II

Foundation for expansion and leverage Conflict detection Completion Regression testing/validation/verification support

foundation Configuration support Structured, comparative search Generalization/ Specialization …

E-Commerce Search E-Commerce Search (starting point Forrester modified by McGuinness)(starting point Forrester 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)