Affiliations: Department of Mathematics, Jamia Millia Isamia (A
Central University), Jamia Nagar, New Delhi-25, India | Department of Mathematics, Indian Institute of
Technology Delhi, Hauz Khas, New Delhi-16, India
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.
