BISC: BISC Seminar-Feb 15; 2002: Intelligent Search Engine Based on Conceptual

From: Masoud Nikravesh (nikravesh@eecs.berkeley.edu)
Date: Sun Jan 20 2002 - 19:25:11 MET

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    Berkeley Initiative in Soft Computing (BISC)
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    Intelligent Search Engine Based on Conceptual Semantic Indexing

    BISC Seminar

    Masoud Nikravesh
    EECS-CS Division
    University of California-Berkeley
    Feb. 15, 2002
    320 Soda Hall
    4:00-5:30 p.m.

    Abstract:

    World Wide Web search engines have become the most heavily-used online services,
    with millions of searches performed each day. Their
    popularity is due, in part, to their ease of use. The central tasks for the most
    of the search engines can be summarize as 1) query or user information
    request- do what I mean and not what I say!, 2) model for the Internet, Web
    representation-web page collection, documents, text, images, music, etc,
    and 3) ranking or matching function-degree of relevance, recall, precision,
    similarity, etc.

    Design of any new intelligent search engine should be at least based on two main
    motivations:

    i The web environment is, for the most part, unstructured and imprecise. To
    deal with information in the web environment what is needed is a logic
    that supports modes of reasoning which are approximate rather than exact. While
    searches may retrieve thousands of hits, finding decision-relevant
    and query-relevant information in an imprecise environment is a challenging
    problem, which has to be addressed.

    ii Another, and less obvious, is deduction in an unstructured and imprecise
    environment given the huge stream of complex information.

    One can use clarification dialog, user profile, context, and ontology, into an
    integrated frame work to design a more intelligent search engine. The
    model will be used for intelligent information and knowledge retrieval through
    conceptual matching of text. The selected query doesn't need to
    match the decision criteria exactly, which gives the system a more human-like
    behavior. The model can also be used for constructing ontology or
    terms related to the context of search or query to resolve the ambiguity. The
    new model can execute conceptual matching dealing with
    context-dependent word ambiguity and produce results in a format that permits
    the user to interact dynamically to customize and personalized its
    search strategy.

    It is also possible to automate ontology generation and document indexing using
    the terms similarity based on Conceptual-Latent Semantic Indexing
    Technique (CLSI). Often time it is hard to find the "right" term and even in
    some cases the term does not exist. The ontology is automatically
    constructed from text document collection and can be used for query refinement.
    It is also possible to generate conceptual documents similarity map
    that can be used for intelligent search engine based on CLSI, personalization
    and user profiling. The user profile is automatically constructed from
    text document collection and can be used for query refinement and provide
    suggestions and for ranking the information based on pre-existence user
    profile.
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