BISC: Zadeh/ BISC Seminar: corrected Dates

From: Masoud Nikravesh (nikravesh@eecs.berkeley.edu)
Date: Wed Jan 23 2002 - 19:14:37 MET

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    *********************************************************************
    Berkeley Initiative in Soft Computing (BISC)
    *********************************************************************

    We apologize for inconvenience. The correct dates for the Next Three BISC
    Lectures are:
    =============================

    The Next Three BISC Presentations:

    ==================================

    1. Building Better Search Engine
    BISC Distinguished Lecture Series
    Peter Norvig
    Director of Machine Learning
    Jan 31, 2002
    320 Soda Hall
    4:00-5:30 p.m.

    2 A Prototype-Centered Approach to Adding Deduction
    Capability to Search Engines -- The Concept of Protoform
    BISC Seminar
    Lotfi A. Zadeh
    Feb 7, 2002
    320 Soda Hall
    4:00-5:30 p.m.

    3. Intelligent Search Engine Based on Conceptual Semantic Indexing

    BISC Seminar

    Masoud Nikravesh
    EECS-CS Division
    University of California-Berkeley
    Feb. 14, 2002
    320 Soda Hall
    4:00-5:30 p.m.

    =================================================================

    A Prototype-Centered Approach to
    Adding Deduction Capability to Search Engines -- The Concept of Protoform

    BISC Seminar
    Lotfi A. Zadeh
    EECS-CS Division
    University of California-Berkeley
    Feb. 7, 2002
    320 Soda Hall
    4:00-5:30 p.m.

    Abstract:

    Existing search engines have many remarkable capabilities. But what is not among
    them is the deduction capability -- the capability to answer a
    query by drawing on information which resides in various parts of the knowledge
    base or is augmented by the user.

    Limited progress toward a realization of deduction capability is achievable
    through application of methods based on bivalent logic and standard
    probability theory. But to move beyond the reach of standard methods it is
    necessary to change direction. In the approach which is outlined, a concept
    which plays a pivotal role is that of a prototype -- a concept which has a
    position of centrality in human reasoning, recognition, search and decision
    processes.

    Informally, a prototype may be defined as a sigma-summary, that is, a summary of
    summaries. With this definition as the point of departure, a
    prototypical form, or protoform, for short, is defined as an abstracted
    prototype. As a simple example, the protoform of the proposition "Most
    Swedes are tall" is "QA's are B's," where Q is a fuzzy quantifier, and A and B
    are labels of fuzzy sets.

    Abstraction has levels, just as summarization does. For example, in the case of
    "Most Swedes are tall," successive abstracted forms are "Most A's
    are tall," "Most A's are B's" and "QA's are B's."

    At a specified level of abstraction, propositions are PF-equivalent if they have
    identical protoforms. For example, propositions "Usually Robert
    returns from work at about 6 pm" and "In winter, the average daily temperature
    in Berkeley is usually about fifteen degrees centigrade," are
    PF-equivalent. The importance of the concepts of protoform and PF-equivalence
    derives in large measure from the fact that they serve as a basis for
    knowledge compression.

    A knowledge base is assumed to consist of a factual database, FDB, and a
    deduction database, DDB. In both FDB and DDB, knowledge is assumed
    to fall into two categories: (a) crisp and (b) fuzzy. Examples of crisp items of
    knowledge in FDB might be: "Height of the Eiffel tower is 324m" and
    "Paris is the capital of France." Examples of fuzzy items might be "Most Swedes
    are tall," and "California has a temperate climate." Similarly, in
    DDB, an example of a crisp rule might be "If A and B are crisp convex sets, then
    their intersection is a crisp convex set." An example of a fuzzy rule
    might be "If A and B are fuzzy convex sets, then their intersection is a fuzzy
    convex set." A fuzzy rule may be a crisp assertion about fuzzy sets or a
    fuzzy assertion about crisp sets or a fuzzy assertion about fuzzy sets.

    The deduction database is assumed to consist of a logical database and a
    computational database, with the rules of deduction having the structure of
    protoforms. An example of a computational rule is "If Q1 A's are B's and Q2 (A
    and B)'s are C's," then "Q1 Q2 A's are (B and C)'s," where Q1 and
    Q2 are fuzzy quantifiers, and A, B and C are labels of fuzzy sets. The number of
    rules in the computational database is assumed to be very large in
    order to allow a chaining of rules that may be query-relevant.

    A very simple example of deduction in the prototype-centered approach-an example
    which involves string matching but no chaining -- is the
    following. Suppose that a query is "How many Swedes are very tall?" A protoform
    of this query is: ?Q A's are B^^2, where Q is a fuzzy quantifier
    and B^^2 is assumed to represent the meaning of "very B," with the membership
    function of B^^2 being the square of the membership function of B.
    Searching DDB, we find the rule "If Q A's are B then Q^0.5 A's are B^^2," whose
    consequent matches the query, with ?Q instantiated to Q^.5, A to
    "Swedes" and B to "tall." Furthermore, in FDB, we find the fact "Most Swedes are
    tall," which matches the antecedent of the rule, with Q instantiated
    to "Most." A to "Swedes" and B to "tall." Consequently, the answer to the query
    is "Most^0.5 Swedes are very tall," where the membership function
    of "Most^0.5" is the square root of Most in fuzzy arithmetic.

    The concept of a prototype is intrinsically fuzzy. For this reason, the
    prototype-centered approach to deduction is based on fuzzy logic and
    perception-based theory of probabilistic reasoning, rather than on bivalent
    logic and standard probability theory.

    What should be underscored is that the problem of adding deduction capability to
    search engines is many-faceted and complex. It would be
    unrealistic to expect rapid progress toward its solution.

    * Lotfi A. Zadeh is Professor in the Graduate School and director, Berkeley
    initiative in Soft Computing (BISC), Computer Science Division and the
    Electronics Research Laboratory, Department of EECS, University of California,
    Berkeley, CA 94720-1776; Telephone: 510-642-4959; Fax:
    510-642-1712;E-Mail: zadeh@cs.berkeley.edu. Research supported in part by ONR
    Contract N00014-99-C-0298, NASA Contract NCC2-1006,
    NASA Grant NAC2-117, ONR Grant N00014-96-1-0556, ONR Grant FDN0014991035, ARO
    Grant DAAH 04-961-0341 and the BISC Program of
    UC Berkeley.

    ================================================================
    ===============================================================
    Intelligent Search Engine Based on Conceptual Semantic Indexing

    BISC Seminar

    Masoud Nikravesh
    EECS-CS Division
    University of California-Berkeley
    Feb. 14, 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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