WEB INTELLIGENCE: CONCEPT-BASED WEB SEARCH
Masoud Nikravesh, Tomohiro Takagi · Series in machine perception and artificial intelligence · 2004
Abstract: Retrieving relevant information is a crucial component of cased-based reasoning systems for Internet applications such as search engines. The task is to use user-defined queries to retrieve useful information according to certain meas-ures. Even though techniques exist for locating exact matches, finding relevant partial matches might be a problem. The objective of this paper is to develop an intelligent computer system with some deductive capabilities to conceptually clus-ter, match and rank pages based on predefined linguistic formulations and rules defined by experts or based on a set of known homepages. The Conceptual Fuzzy Set (CFS) model will be used for intelligent information and knowledge retrieval through conceptual matching of both text and links (here defined as “Concept”). The selected query doesn’t need to match the decision criteria exactly, which gives the system a more human-like behavior. The model can be used for intelli-gent information and knowledge retrieval through Web-connectivity-based clus-tering. We will also present the integration of our technology into commercial search engines such as Google ™ as a framework that can be used to integrate our model into any other commercial search engines, or 1