Affiliations: The Department of Computer Engineering, Ajou
University, Suwon, 442-749, Korea (e-mail: minkoo@madang.ajou.ac.kr) | The Department of Computer Science & Engineering,
University of Nebraska, Lincoln, NE 68588, USA | The Center for Advanced Computer Studies, University
of Louisiana, Lafayette, LA 70504, USA
Abstract: One of the essential goals in information retrieval is to bridge the
gap between 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 goal.
This approach involves the use of production rules to capture user-query
concepts (or topics). In RUBRIC, a set of related production rules is
represented as an AND/OR tree, or alternatively by a disjunction of Minimal
Term 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 expression
ignores the term-term association unless it is explicitly represented in the
tree, the terminological gap between users' queries and their information needs
may still remain. To solve this problem, we adopt the generalized 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 based extended Boolean
model. The results are compared to the original RUBRIC model based on
recall-precision.
