The vocabulary problem in human-system communication
Communications of the ACM
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Cost-based abduction and MAP explanation
Artificial Intelligence
Applying Bayesian networks to information retrieval
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence - Special issue: artificial intelligence research in Japan
Space-efficient inference in dynamic probabilistic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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When a search engine user becomes interested in a new area for him/herself, it is difficult for the user to enter a query precisely expressing the interest or to select areas including the interest, because he/she is just a beginner of the interest. This paper presents a system called Index Navigator, which tells areas a user is interested in, keywords he/she should enter as a query, and documents concerning his/her interest. A tough problem for such a system is to understand the user's interest from the query he/she entered. Index Navigator employs an inference method called Cost-based Cooperation of Multi-Abducers (CCMA), for understanding a user's interest from the history of the user's queries (expression of interest in iincomplete keywords), even if the changing speed of the user's interest can not be estimated. With this device, Index Navigator guided the user to areas, keywords and documents relevant to his/her interest, according to the experimental results.