BISC: BISC Seminar: September 6, 2001

From: Masoud Nikravesh (nikravesh@eecs.berkeley.edu)
Date: Wed Sep 05 2001 - 11:04:38 MET DST

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    Berkeley Initiative in Soft Computing (BISC)
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    Fuzzy Conceptual Matching
    Tool for Intelligent Knowledge Management and Discovery in the Internet

    BISC Seminar
    Masoud Nikravesh
    BISC Program
    EECS Department, CS Division
    UC Berkeley
    September 6, 2001
    373 Soda Hall
    4:00-5:30 p.m.

    Abstract:

    Given the ambiguity and imprecision of the "concept" in the Internet, which may
    be described by both textual and image information, the use of Fuzzy Conceptual
    Matching (FCM) is a necessity for search engines. In the FCM approach, the
    "concept" is defined by a series of keywords with different weights depending on
    the importance of each keyword. Ambiguity in concepts can be defined by a set of
    imprecise concepts. Each imprecise concept in fact can be defined by a set of
    fuzzy concepts. The fuzzy concepts can then be related to a set of imprecise
    words given the context. Imprecise words can then be translated into precise
    words given the ontology and ambiguity resolution through clarification dialog.
    By constructing the ontology and fine-tuning the strength of links (weights), we
    could construct a fuzzy set to integrate piecewise the imprecise concepts and
    precise words to define the ambiguous concept.

    In this presentation, first we will present the role of the fuzzy logic in the
    Internet. Then we will present an intelligent model that can mine the Internet
    to conceptually match and rank homepages based on predefined linguistic
    formulations and rules defined by experts or based on a set of known homepages.
    The FCM model will be used for intelligent information and knowledge retrieval
    through conceptual matching of both text and images (here defined as "Concept").
    The FCM can also be used for constructing fuzzy ontology or terms related to the
    context of the query and search to resolve the ambiguity. Finally, this model
    can be used to calculate conceptually the degree of match to the object or
    query.

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