While concept-based methods for information retrievul can provide improved performance over more conventional techniques, they require large amounts of effort to acquire the concepts and their qualitative and quantitative relationships. This paper discusses an aschitecture for probabilistic concept-based information retrieval which addzesses the knowledge acquisition problem. The architecture makes use of the probabilistic networks technology for representing and reasoning about concepts and ineludes a knowledge acquisition component which partia]]y automates the construction of concept know1-edge bases from data.We describe two experiments that apply the architecture to the task of retrieving documents about tenorism from a set of documents from the Reuters news serv/ce. The experiments provide positive evidence that the architecture design is feasible and that there are advantages to concept-based methods.
I IntroductionIn this paper we describe some preliminary research on the use of probabilistic networks for information retrieval. In particular, we introduce an architecture • This work was funded by ADS' Internal Research sad Development Prrogran~ for probabilistic, concept-based information retrieval (henceforth PCIR) that can be used to automatically generate relationships between concepts, and then reason about them given the evidence provided by individual documents. This is in contrast to our previons research on concept-based methods [12, 16,17], in which we were primarily interested in issues related to ~easonin8 about concepts and not so much concerned with issues related to the construction of the relationships. Our lons-term goal, however, ¢ontin-ues to be the development of techniques that can form the basis for an effective system to assist users in sortin 8 throu8h lurge volumes of time sensitive material. We have in mind such applications as the day-to-day momtorin 8 of newswires for specific topics of interest.
This article contains a detailed description of the knowledge representation used in RUBRIC, our concept-based, full-text information retrieval system. Since we view information retrieval as a process of evidential reasoning, the RUBRIC knowledge representation is designed to help maintain a clear distinction between descriptions of domain concepts, the documentary evidence for those concepts, and the beliefs about concepts that can be sustained on the basis of the evidence. This article is a consolidation of our earlier work in this area, containing, as it does, a revised language for defining concepts, an extended view of the role of evidence, and a formally grounded model of belief.
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