Abstract
Word based searches for relevant information from texts retrieve a huge collection and burden the user with information overload. Ontology based text information retrieval can perform concept-based search and extract only relevant portions of text containing concepts that are present in the query or those that are semantically linked to query concepts. While these systems have better precision of retrieval than general-purpose search engines, problems arise with those domains where ontological concepts cannot be unambiguously described using precise property descriptors. Besides, the ontological descriptors may not exactly match text descriptions or the user given descriptors in query. In such situations, uncertainty based reasoning principles can be applied to find approximate matches to user queries. In this paper we have presented a framework to enhance traditional ontological structures with fuzzy descriptors. The fuzzy ontology structure has been used to locate and extract both precise and imprecise descriptions of concepts from Web documents and then store them in a structured knowledge base. The design of the structured knowledge base, which in our case is a database, is also derived from the underlying fuzzy ontology representing the domain. User queries are processed in two stages. In the first stage, precise SQL queries are formulated and processed over the knowledge base to find a possible answer set. In the second stage, fuzzy reasoning is applied to compute the relevance of the answers in the answer set with respect to the query. We have provided experimental validation of the approach through knowledge-extraction and query processing executed over a diverse set of domains.