Describes the use of relevance feedback to select additional search terms and discusses the extraction of these terms from a maximum spanning tree connecting all terms in the index term vocabulary; retrieval effectiveness for different spanning trees is shown to be similar. Eight references are included. (Author/BK)

Proposes a means for determining the similarity between search request formulations in online information retrieval systems, and discusses the use of similarity measures for clustering search formulations and document files in such systems. Experimental results using the proposed methods are presented in three tables. A reference list is provided.…

Discusses precision and recall in information science and proposes a new model based on fractal geometry for clusters of relevant documents. Search strategies for retrieving a group of relevant documents are reviewed; fractal sets and chaotic processes are described; and the new model is explained. (Contains 43 references.) (LRW)

This paper focuses on clustering a World Wide Web site (i.e., the 1998 World Cup Soccer site) into groups of documents that are predictive of future user accesses. Two approaches were developed and tested. The first approach uses semantic information inherent in the documents to facilitate the clustering process. User access history is then used…

Discussion of relevancy in information retrieval systems focuses on an analysis of the efficiency of various cluster-based retrieval (CBR) strategies. A method for combining CBR and inverted index search is proposed that is cost effective in terms of time efficiency; and results of experiments are reported. (Contains 32 references.) (LRW)

Reports research results into a methodology for determining similarity between queries characterized by Boolean search request formulations and discusses similarity measures for Boolean combinations of index terms. Rationale behind these measures is outlined, and conditions ensuring their equivalence are identified. Results of an experiment…

Presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records from a test database at the University of California at Berkeley Library School library. Classification clustering and matching techniques are described. (44 references) (LRW)