The goal of information retrieval systems is to maximize the number of relevant documents returned for each query. Keyword IR systems often return irrelevant documents because matching keywords is imprecise. Precision can be improved by matching concepts, keywords for which the intended meaning is known. This article describes the algorithms and data structures needed to implement a concept IR system, how concepts can be used to enhance keyword queries, and describes a prototype user interface for a concept IR system.
Researchers and students need information retrieval tools that will support them in their work. To provide effective support, such tools must be able to track changing interests and automatically suggest new, possibly unrelated, information that might be helpful. This article describes an approach to representing a user interest profile using a genetic algorithm, a form of evolutionary model. The profile is maintained with user feedback and genetic operators and initial testing with a database of academic journal abstracts has found this to be a promising technique.
Document maps are a new visualization tool that help users filter information retrieval search results. Documents are compactly represented by a square matrix that presents document similarity and frequently occurring search items. Evaluation has shown that document maps enable users to filter search results quickly and accurately.