SEECS Colloquium

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.

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. This model can be used to calculate conceptually the degree of match to the object or query. We will also present the integration of our technology into commercial search engines such as Google ™ and Yahoo! as a framework that can be used to integrate our model into any other commercial search engines, or development of the next generation of search engines.

Dr. Masoud Nikravesh received his BS from Abadan Institute of Technology, MS and PhD in Chemical Engineering from the University of South Carolina (August 1993 and Dec.1994) and received full scholarship from University of California, Berkeley for his second Ph.D. in Material Sciences and Mineral Engineering Department for Fall 1994, when he decided to start his scientific carrier as Postdoc researcher in Spring 1995 at University of California-Berkeley and Lawrence Berkeley National Lab as joint appointment. Dr. Nikravesh is the BISC Associate Director, BTExact technology Senior Fellow and BISC Program Manager in the Computer Science Division, Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley and Research Scientist in the Imaging and Informatics Group at NERSC (National Energy Research Scientific Computing Division, Lawrence Berkeley National Laboratory. In addition, he is serving as the Associate Director (Co-founder) of Zadeh Institute for Information Technology (Information Technology) and Chairs of BISC-Earth Sciences, BISC-Fuzzy Logic and Internet, and BISC-Recognition Technology Groups. His credentials have led to front-page news at Lawrence Berkeley National Laboratory News and headline news at the Electronics Engineering Times. Dr. Nikravesh is the LBNL-NERSC (National Energy Research Scientific Computing Division) representative to the DiMI Executive Committee.