Enhancing concept-based retrieval based on minimal term sets


ALSAFFAR A., DEOGUN J., RAGHAVAN V., Sever H.

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, cilt.14, ss.155-173, 2000 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 14
  • Basım Tarihi: 2000
  • Doi Numarası: 10.1023/a:1008783718847
  • Dergi Adı: JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • Sayfa Sayıları: ss.155-173

Özet

There is considerable interest in bridging the terminological gap that exists between the way users prefer to specify their information needs and the way queries are expressed in terms of keywords or text expressions that occur in documents. One of the approaches proposed for bridging this gap is based on technologies for expert systems. The central idea of such an approach was introduced in the context of a system called Rule Based Information Retrieval by Computer (RUBRIC). In RUBRIC, user query topics (or concepts) are captured in a rule base represented by an AND/OR tree. The evaluation of AND/OR tree is essentially based on minimum and maximum weights of query terms for conjunctions and disjunctions, respectively. The time to generate the retrieval output of AND/OR tree for a given query topic is exponential in number of conjunctions in the DNF expression associated with the query topic. In this paper, we propose a new approach for computing the retrieval output. The proposed approach involves preprocessing of the rule base to generate Minimal Term Sets (MTSs) that speed up the retrieval process. The computational complexity of the on-line query evaluation following the preprocessing is polynomial in m. We show that the computation and use of MTSs allows a user to choose query topics that best suit their needs and to use retrieval functions that yield a more refined and controlled retrieval output than is possible with the AND/OR tree when document terms are binary. We incorporate p-Norm model into the process of evaluating MTSs to handle the case where weights of both documents and query terms are non-binary.