Concept based retrieval by minimal term sets

The problem of bridging the terminological gap between the way users prefer to specify their information needs and the way queries are formulated in terms of words or text expressions is of considerable interest. The central ideas of existing approaches based on expert systems technology were introduced in the context of a system called RUBRIC. In RUBRIC, user query topics (or concepts) are captured in a rule base and the rule base is represented as an AND/OR tree. Determining the retrieval output by evaluation of the AND/OR tree is exponential in m, where m is the maximum number of conjunctions in the DNF expression associated with a query topic. In this paper, we propose a method of computing retrieval output that involves the preprocessing of the rule base to generate what we call Minimal Term Sets (MTS) that enhances the computations needed for retrieval. The computational complexity associated with the proposed approach is polynomial in m. We also show that MTSs can provide additional advantages for the users by enabling them to (i) choose query topics that best suit their needs from among existing ones and (ii) use retrieval functions that yield more refined and controlled retrieval output than is possible with the AND/OR tree.