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IR to UIR Future Directions in Information Retrieval

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Title: IR to UIR Future Directions in Information Retrieval


1
IR to UIRFuture Directions in Information
Retrieval
  • Rohini K. Srihari
  • Department of Computer Science and Engineering
  • State University of New York at Buffalo

2
Future of Information Retrieval
  • Objective More productive IR sessions by
    combining search with discovery
  • Key Challenges
  • Response customized to user needs (HARD track at
    TREC)
  • Granularity of response
  • User preferences
  • Genre of response (general news,
    subjective/opinion, background etc.)
  • Interactive IR
  • Search information extraction text
    mining/discovery follow-up search
  • Hybrid Representations for IR
  • Incorporate both traditional word models (e.g.
    vector space) and concept/association models
    enabled by information extraction
  • Permit text mining

3
CCG for FAA Website
Lightning
AIRPLANE
Fuel Tank
HAZARD
Wiring
AVIATION
Statistics
Fuel Tank Ignition events
Windshear
ACCIDENT
Pumps
Air_traffic_ _control_tower
Ice/snow
Small Bomb
In-flight fire
Previously viewed page(s)
Fatalities
hull losses
Runway Incursions
requested page
UIR module determines that these two documents
reveal new association between wiring and
accidents.
4
Unapparent Information Revelation (UIR)
  • Document collections may reveal interesting
    information other than what is explicitly stated
  • E.g. interesting links between gene names, genes
    and diseases
  • E.g. Links between people and organizations
  • Hidden information a consequence of multiple
    sources and authors working independently
  • Users, e.g. researchers, analysts need to
    discover links
  • Reverse IR system is given relevant documents,
    trying to generate complex query/narrative
  • User has already generated a set of interesting
    documents through some searches what complex
    query do these represent?
  • Required set of automated tools that will
  • expose interesting links, generate corresponding
    document set
  • Permit refinement of queries based on above

5
Integrated IR UIR System
6
Components of a UIR System
  • Domain map representing major concepts and
    associations
  • E.g., UMLS, intelligence knowledge bases
  • Automatic extraction of key concepts and
    associations from a target document set
  • Concepts include named entities, phrases that are
    subjects/objects of events
  • Associations include both named relationships
    such as affiliation, age, etc. Also includes
    un-named relationships based on proximity of
    concepts in a sentence, paragraph, document etc.
  • Concept Chain Graph
  • Probabilistic network with nodes representing
    concepts, edges representing associations
  • Document is viewed as a sub-graph footprint is a
    measure of information revealed
  • Intuitive visualization, navigation
  • Text mining module operating on the CCG
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