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A probabilistic theory of indexing and similarity measure based on cited and citing documents

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  • K. L. Kwok

Abstract

A new model of viewing a document based on the citingcited relationship between documents is introduced. Using Bayes' decision theory, it is shown how a source document may be indexed and weighted by its set of relevant cited or citing document features, corresponding to a one pass relevance feedback Model 1 (probabilistic indexing) or Model 2 (probabilistic retrieval) system of [8]. Once every document in a collection has been so indexed, various forms of similarity measures based on probability of topical relevance between documents are derivable, including asymmetric, symmetric, and the relationship with Model 3 of [8]. Applications to retrieval and document clustering are also discussed.

Suggested Citation

  • K. L. Kwok, 1985. "A probabilistic theory of indexing and similarity measure based on cited and citing documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 36(5), pages 342-351, September.
    • Handle: RePEc:bla:jamest:v:36:y:1985:i:5:p:342-351
    DOI: 10.1002/asi.4630360510
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