Researchers in a scientific community or organizational members in a company are often overwhelmed by the amount of current information, the subject and computer knowledge required to access this information, and the constant influx of even larger amounts of new information. Using an electronic community system as a testbed the aim of the research is to address specifically the information sharing, retrieval, and filtering problems through the use of a variety of AI and statistical techniques, including knowledge acquisition, automatic thesaurus generation, and neural network computing. A Concept Space is designed to represent high- level community knowledge such as topics, subjects, techniques, and people in terms of nodes and their weighted relationships in a structure that is akin to a semantic network. This Concept Space can be perceived as a high-level conceptual description of the basic information items that reside in an Information Space. It is created mainly from a knowledge acquisition process involving subject experts and from an automatic cluster analysis of documents stored in the community's online, textual databases. Two kinds of system interface are considered both making extensive use of the Concept Space. A browsing interface permits users to traverse this weighted concept network themselves, select concepts of interests, and access its underlying information. An activation interface based on a Hopfield network spreading activation algorithm traverses the network automatically and identify concepts most relevant to searchers' needs, with very little searcher intervention. Both types of interface are designed to provide user-friendly access, to facilitate learning, and to avoid the need for extensive user computing knowledge. This research is designed to provide insights for the development of more timely, comprehensive, and accessible electronic community systems and computer-based organizational memory.
