Transcript Ontologies

Ontology-enhanced retrieval (and
Ontology-enhanced applications)
Deborah L. McGuinness
Associate Director and Senior Research Scientist
Knowledge Systems Laboratory
Stanford University
Stanford, CA 94305
650-723-9770
[email protected]
(FindUR,CLASSIC,PROSE work supported by AT&T Labs Research, Florham
Park, NJ, OntoBuilder work supported by VerticalNet,
Chimaera, Ontolingua, JTP supported by DARPA)
One Conceptual Search
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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
Human Manipulation/Interpretation
Human
Human
“I know what was input”
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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
not 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
Agent Manipulation/Interpretation
Human
Agent
Agent
“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
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One Approach… start simple
from 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
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FindUR 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
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FindUR Architecture
Content (Web
Pages, Documents,
Databases) (
Content to Search:
Content
Classification
Search and
Representation
Technology:
Search
Engine
Classic
Domain
User Interface:
P-CHIP
Research Site
Technical Memorandum
Calendars (Summit 2005,
Research)
Yellow Pages (Directory Wes
Newspapers (Leader)
AT&T Solutions
Worldnet Customer Care
Search
Parameters
Knowledge
Collaborative Topic
Building
Tool
Verity Topic Sets
Query Input
Results
(std. format)
Results
(domain spec.)
Verity SearchScript,
Javascript, HTML,
CGI
OntologyBuilder
Configuration
http://www.research.att.com/sw/tools/classic/tm/ijcai-95-with-scenario.html
Ontology Creation and
Maintenance Environment Needs
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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,…)
Conclusion
With background ontologies and the
appropriate environments, we can move
from simple ontology-enhanced
applications to the next generation web
Pointers
 FindUR:
www.research.att.com/~dlm/findur
 OntoBuilder/OntoServer:
http://www.ksl.stanford.edu/people/dlm/papers/ontologyBui
lderVerticalNet-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/