Information Granulation for the Design of Granular Information Retrieval Systems

With the explosive growth of the amount of information stored on computer networks such as the Internet, it is increasingly more difficult for information seekers to retrieve relevant information. Traditional document ranking functions employed by Internet search engines can be enhanced to improve the effectiveness of information retrieval (IR). This paper illustrates the design and development of a granular IR system to facilitate domain specific search. In particular, a novel computational model is designed to rank documents according the searchers’ specific granularity requirements. The initial experiments confirm that our granular IR system outperforms a classical vector-based IR system. In addition, user-based evaluations also demonstrate that our granular IR system is effective when compared with a well-known Internet search engine. Our research work opens the door to the design and development of the next generation of Internet search engines to alleviate the problem of information overload

Mining Fuzzy Domain Ontology from Textual Databases

Mining search engine query log is a new method for evaluating web site link structure and information architecture. In this paper we propose a new query-URL co-clustering for a web site useful to evaluate information architecture and link structure. Firstly, all queries and clicked URLs corresponding to particular web site are collected from a query log as bipartite graph, one side for queries and the other side for URLs. Then a new content free clustering is applied to cluster queries and URLs concurrently. Afterwards, based on information entropy, clusters of URLs and queries will be used for evaluating link structure and information architecture respectively. Data sets of different web sites have been extracted from a huge query log to evaluate our method, and experiments show promising result

Fuzzy Information Retrieval Model Based On Multiple Related Ontologies

With the semantic web progress, encoding of knowledge bases as ontologies has increased. Information retrieval applications are employing this knowledge organization to enhance quality of results by returning documents semantically related and relevant to initial user's query. The proposed fuzzy information retrieval model retrieves information providing a framework to encode a knowledge base composed of multiple related ontologies whose relationships are expressed as fuzzy relations. This knowledge organization is used in a novel method to expand the user initial query and to index the documents in the collection. The model allows the ontologies, as well as the relationships among their concepts, to be represented independently. 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