This project describes the need for an initiative to design an intelligent search engine based on two main motivations:
The web environment is, for the most part, unstructured and imprecise. To deal with information in the web environment, we need a logic that supports modes of reasoning that 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. Another less obvious issue is deduction in an unstructured and imprecise environment given the huge stream of complex information.
As a result, intelligent search engines with growing complexity and technological challenges are currently being developed. This requires new technology in terms of understanding, development, engineering design, and visualization. While the technological expertise of each component becomes increasingly complex, there is a need for better integration of each component into a global model adequately capturing the imprecision and deduction capabilities.
The initiative will bring an integrated approach to perception-based information processing and retrieval, including intelligent search engines by integrating the various components and achievements of its team members. The objective of this initiative is to develop an intelligent computer system with deductive capabilities to conceptually 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 images (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 CFS can also be used for constructing fuzzy ontology or terms related to the context of search or query to resolve the ambiguity. We intend to combine the expert knowledge with soft computing tools of Berkeley groups. Expert knowledge needs to be partially converted into artificial intelligence that can better handle the huge information stream. In addition, sophisticated management work-flow needs to be designed to make optimal use of this information. We believe our current team is unique in the world of perception-based information processing and analysis in tackling this problem. We intend no less than changing the face and practice of the intelligent search engines for complex unstructured dynamic systems and imprecise environments such as the Internet. 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.