On modeling information retrieval with probabilistic inference

This article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic in...

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Bibliographic Details
Published in:ACM transactions on information systems 1995-01, Vol.13 (1), p.38-68
Main Authors: Wong, S. K. M., Yao, Y. Y.
Format: Article
Language:English
Subjects:
Artificial intelligence
Computerized information retrieval
Computing methodologies
Information retrieval
Information retrieval query processing
Information systems
Knowledge representation and reasoning
Probabilistic reasoning
Probabilistic retrieval
Searching
Vagueness and fuzzy logic
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Summary:This article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic inference. This new approach provides a common conceptual and mathematical basis for many retrieval models, such as the Boolean, fuzzy set, vector space, and conventional probabilistic models. Within this framework, the underlying assumptions employed by each model are identified, and the inherent relationships between these models are analyzed. Although this article is mainly a theoretical analysis of probabilistic inference for information retrieval, practical methods for estimating the required probabilities are provided by simple examples.
ISSN:1046-8188
1558-2868
DOI:10.1145/195705.195713