Describes exploratory experiments in evaluating and improving a thesaurus through studying its effect on retrieval. A formula was developed to measure the conceptual distance between queries and documents encoded as sets of thesaurus terms. Comparison to the performance of people judging conceptual distance shows that the formula accurately…

Reviews aspects of the relationship between machine learning and information retrieval. Highlights include learning programs that extend from knowledge-sparse learning to knowledge-rich learning; the role of the thesaurus; knowledge bases; artificial intelligence; weighting documents; work frequency; and merging classification structures. (78…

Describes a study funded by the Commission of the European Community that applied document retrieval algorithms to the "Excerpta Medica" database using the EMTREE thesaurus. Nonhierarchical relations were added to enrich the thesaurus, and document ranking is discussed in terms of the conceptual distance between the documents and the…

Discussion of collaborative learning focuses on the MUCH (Many Using and Creating Hypermedia) System, developed at the University of Liverpool (England) for designing and implementing collaborative learning systems. Examples of applications of the MUCH System for nursing education and for computer science education are described, including the use…

Describes the indexing concept of entry terms, and evaluates existing terms in the Medical Subject Headings by comparing them to the results of an automatic indexer. The results of experimental testing of a strategy for creating entry terms using the algorithms of the automatic indexer are presented. (31 references) (CLB)