Presents a model that places Boolean database queries into conjunctive normal form, thereby allowing probabilistic ranking of documents and the incorporation of relevance feedback. Experimental results compare the performance of a sequential learning probabilistic retrieval model with the proposed integrated Boolean probabilistic model and a fuzzy…

A theoretical framework used to derive a weighting mechanism for ranking output documents in Boolean retrieval systems is described in detail. A simple illustrative example is included, and the implications of such an approach are discussed. (64 references) (Author/CLB)

Describes rough sets theory and discusses the advantages it offers for information retrieval, including the implicit inclusion of Boolean logic, term weighting, ranked retrieval output, and relevance feedback. Rough set formalism is compared to Boolean, vector, and fuzzy models of information retrieval and a small scale evaluation of rough sets is…

Describes the three subsystems of an information retrieval system (document descriptions, queries, and matching algorithms) and argues that the interdependency of these subsystems requires adaptation for the system to perform when any component changes. An algorithm for redescribing documents, in response to changes in queries and retrieval rules,…

Discusses the motivation for integrating information retrieval and database management systems, and proposes a probabilistic retrieval model in which records in a file may be composed of attributes (formatted data items) and descriptors (content indicators). The details and resolutions of difficulties involved in integrating such systems are…

Provides an overview of methods for improving the effectiveness of Boolean retrieval systems, including document clustering, relevance feedback, weighted term searching, ranked output, and fuzzy set theory. The discussion of experimental studies with the SMART and SIRE systems focuses on the implementation and results of these methods. (69…