Concept based retrieval using generalized retrieval functions

One of the essential goals in information retrieval is to bridge the gap be tween the way users would prefer to specify their information needs and the way queries are required to be expressed. Rule Based Information Retrieval by Computer (RUBRIC) is one of the approaches proposed to achieve this goa l. This approach involves the use of production rules to capture user-query concepts (or topics). In RUBRIC, a set of related production rules is repr esented as an AND/OR tree, or alternatively by a disjunction of Minimal Ter m Sets (MTSs). The retrieval output is determined by the evaluation of the weighted Boolean expressions of the AND/OR tree, and processing efficiency can be enhanced by employing MTSs. However, since the weighted Boolean expr ession ignores the term-term association unless it is explicitly represente d in the tree, the terminological gap between users' queries and their info rmation needs may still remain. To solve this problem, we adopt the general ized vector space model (GVSM) and the p-norm based extended Boolean model. Experiments are performed for two variations of the RUBRIC model, extended with GVSM, as well as for the integrated use of RUBRIC with the p-norm bas ed extended Boolean model. The results are compared to the original RUBRIC model based on recall-precision.