Ontology-enhanced retrieval (and Ontology-enhanced retrieval (and Ontology-enhanced applications)Ontology-enhanced applications)

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

(FindUR,CLASSIC,PROSE work supported by AT&T Labs Research, Florham (FindUR,CLASSIC,PROSE work supported by AT&T Labs Research, Florham Park, NJ, OntoBuilder work supported by VerticalNet,Park, NJ, OntoBuilder work supported by VerticalNet,

Chimaera, Ontolingua, JTP supported by DARPA)Chimaera, Ontolingua, JTP supported by DARPA)

One Conceptual SearchOne Conceptual Search

Input is in a natural query language (forms, English, ER diagram …) Query may be transformed (behind the scenes) into a precise query

language with defined semantics Information is at least semi-structured with DL-like markup and also

“exists” in more natural formats and is interoperable Answers returned that are not just the explicit answer to question (but

also the implicit answer to question) Answers return the portion of the content that is of use (not an entire

page of content) Answers may be summarized, abstracted, pruned “Answers” may be services that can take action Interface is interactive and helps users reformulate “unsuccessful”

queries Customizable, extensible, …

Today: Rich Information Source for Today: Rich Information Source for Human Manipulation/InterpretationHuman Manipulation/Interpretation

Human

Human

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

Global documents and terms indexed and available for search Search engine interfaces Entire documents retrieved according to relevance (instead of

answers) Human input, review, assimilation, integration, action, etc. Special purpose interfaces required for user friendly applications

The web knows what was input but does little interpretation, manipulation, integration, and action

Information Discovery… but Information Discovery… but not much morenot much more

Human intensive (requiring input reformulation and interpretation)

Display intensive (requiring filtering) Not interoperable Not agent-operational Not adaptive Limited context Limited service

Analogous to a new assistant who is thorough yet lacks common sense, context, and adaptability

Future: Rich Information Source for Future: Rich Information Source for Agent Manipulation/InterpretationAgent Manipulation/Interpretation

Human

Agent

Agent

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

Understand term meaning and user background Interoperable (can translate between applications) Programmable (thus agent operational) Explainable (thus maintains context and can adapt) Capable of filtering (thus limiting display and

human intervention requirements) Capable of executing services

One Approach… start simple One Approach… start simple from embedded basesfrom embedded bases

Recognize the vast amount of information in textual forms…

Enhance “standard” information retrieval by adding some semantics

Use background ontology to do query expansion Exploit ontology to add some structure to IR

search Move to parametric search Move to include inference (in e-commerce setting

moving towards interoperable solutions and configuration

FindUR Challenges/BenefitsFindUR Challenges/Benefits Retrieve documents otherwise missed - Recall More appropriately organize 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

(

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

OntologyBuilderOntologyBuilder

ConfigurationConfiguration

http://www.research.att.com/sw/tools/classic/tm/ijcai-95-with-scenario.htmlhttp://www.research.att.com/sw/tools/classic/tm/ijcai-95-with-scenario.html

Ontology Creation and Ontology Creation and Maintenance Environment NeedsMaintenance Environment Needs

Semi-automatic generation input Diagnostics/Explanation (Chimaera, CLASSIC,…) Merging and Difference (Chimaera, Prompt, Ontolingua, …) Translators/Dumping (Ontolingua, …) Distributed Multi-User Collaboration (OntologyBuilder,…) Versioning (OntologyBuilder,…) Scalability. Reliability, Performance, Availability

(Shoe,OntologyBuilder,…) Security (viewing, updates, abstraction, authoritative sources…) Ontology Library systems (Ontolingua,…) Business needs – internationalization, compatibility with standards

(XML,…)

ConclusionConclusion

With background ontologies and the appropriate environments, we can move from simple ontology-enhanced applications to the next generation web

PointersPointers

FindUR: www.research.att.com/~dlm/findur OntoBuilder/OntoServer:

http://www.ksl.stanford.edu/people/dlm/papers/ontologyBuilderVerticalNet-abstract.html

Deborah McGuinness: www.ksl.stanford.edu/people/dlm CLASSIC: www.research.att.com/sw/tools/classic Chimaera: www.ksl.stanford.edu/software/chimaera/