Transcript FindUR: Knowledge Assisted Search

Ontological Issues for
Knowledge-Enhanced
Search
Deborah McGuinness
AT&T Labs Research
180 Park Ave. Room A215
Florham Park, NJ 07932
[email protected]
http://www.research.att.com/info/dlm
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Ontological Issues - FOIS ‘98
Outline
• Motivation
• Basic Building Blocks (objects,
constructors)
• Inference
• Algorithmic Approaches
• Applications
• Conclusion
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Motivation
Queries miss relevant documents because:
• queries are naive
• documents do not contain “perfect” content
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Solutions
• Augment Documents
- tag all pages with (controlled vocabulary) meta tags
- labor is distributed and must be trained
- approach is unscalable especially if content changes
• Augment Index
- centralized labor cost
- must re-index every time meta tag language changes
• Augment Query (manually)
- requires user training
• Augment Query (automatically)
- no user training or content provider training
- centralized labor cost, no rework needed
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FindUR
• Address issues of recall, rank ordering, and
browsing
• utilizing available knowledge
• in a standard search platform
• deploy, test, and maintain on websites
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Background Knowledge
Supports:
• Retrievals of previously missed relevant
documents
• More relevant retrievals scored higher than
less relevant documents
• Simple user generation and refinement of
queries
• User expectation setting
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FindUR Architecture
Content to Search:
Research Site
Technical Memorandum
Calendars (Summit 2005, Research)
Yellow Pages (Directory Westfield)
Newspapers (Leader)
Internal Sites (Rapid Prototyping)
AT&T Solutions
Worldnet Customer Care
Search Technology:
User Interface:
Content (Web
Pages or Databases
Classification
CLASSIC Knowledge
Representation System
Search
Engine
Domain
Domain
Knowledge
Knowledge
GUI supporting
browsing
and selection
Results
(standard format)
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Content
Results
(domain specific)
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Verity (and
topic sets)
Collaborative
Topic Set Tool
Verity SearchScript,
Javascript, HTML,
CGI, CLASSIC
FindUR improves search by:
• Retrieving previously missed relevant
documents
• More appropriately ordering search results
• Facilitating simple user generation and
refinement of queries
• Setting user expectations about the content
domain
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Selected FindUR implementations:
Electronic Yellow Pages: www.quintillion.com/westfield
Event Calendars: www.quintillion.com/calendar/[summit |westfield]
Medical Information (P-CHIP, POS)
Computer Science Research Information
Competitive Intelligence Sites
Staff Augmentation and Vendor Procurement Info
Network Service Realization
Rapid Prototyping Info and Services
Technical Memorandum Access
Online Newspapers
Hometown Cites
Intellectual Capital
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Common Site Conditions
Short Document Length
Few related content words per document
Unfamiliar vocabularies
Variability in specificity of documents
Inconsistent or irregular meta tagging
Higher (relevance) value for general documents
over specific documents
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Evidence types
Synonyms
Subclasses
Products
Companies
Associated Standards
Key People
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FindUR Architecture
Content to Search:
Content (Web
Pages, Documents,
Databases)
Content
Classification
Search Technology:
Search
Engine
CLASSIC
Domain
User Interface:
GUI supporting
browsing and selection
Verity Topic Sets
Query Input
Results
(std. format)
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Knowledge
Collaborative Topic
Building
Tool
Results
(domain spec.)
Verity SearchScript,
Javascript, HTML,
CGI
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FindUR/Smart Search 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
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FindUR Future 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
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• Evaluate on effectiveness of retrievals, relevance ranking, ease of
query refinement, east of content input into category scheme
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Issues - FOIS ‘98
• Java-based interface
for scalability,
rapid changing, understandability
AT&T Labs Research Site
• FindUR has a taxonomy of background
information which includes “knowledge
representation” as a sub-category of
“artificial intelligence.”
• The category/sub-category relationships are
displayed in the user interface. Users can
construct queries by simply clicking
categories and sub-categories, invoking
background knowledge in the process.
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AT&T Labs Research Site
• With background knowledge the search
returns 696 relevant listings.
• Documents of a more general nature such as
bibliographies and departmental overviews
float higher in the list. Without background
knowledge, a reference manual was the first
retrieval.
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General Nature of Descriptions
a WINE
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a LIQUID
a POTABLE
general categories
grape: chardonnay, ... [>= 1]
sugar-content: dry, sweet, off-dry
color: red, white, rose
price: a PRICE
winery: a WINERY
structured
components
grape dictates color (modulo skin)
harvest time and sugar are related
interconnections
between parts
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General Nature of Descriptions
concept
superconcepts
number
restrictions
roles
value
restrictions
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a WINE
a LIQUID
a POTABLE
general categories
grape: chardonnay, ... [>= 1]
sugar-content: dry, sweet, off-dry
color: red, white, rose
price: a PRICE
winery: a WINERY
structured
components
grape dictates color (modulo skin)
harvest time and sugar are related
interconnections
between parts
Ontological Issues - FOIS ‘98
URLs
FindUR Home Page:
http://www.research.att.com/~dlm/findur
Description Logic Home Page:
http://dl.kr.org/dl
Implemented Description Logic-based systems:
http:/www.ida.liu.se/labs/iislab/people/patla/DL/systems.html
The CLASSIC Knowledge Representation System:
http://www.research.att.com/sw/tools/classic
Deborah McGuinness:
http://www.research.att.com/info/dlm
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Contributors
Tom Beattie
Beth Cataldo
Ihung (Kyle) Chang
Curtis Chen
Lisa Croel
Martha Desmond
Paul Fuoss
Karrie Hanson
Pam Kirkbride
Dave Kormann
Harley Manning
Russ Maulitz
Mark Plotnick
Lori Alperin Resnick
Beth Robinson
Steve Solomon
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