Automatic indexing based on Bayesian inference networks

ABSTRACT

In this paper, a Bayesian inference network model for automatic indexing with index terms (descriptors) from a prescribed vocabulary is presented. It requires an indexing dictionary with rules mapping terms of the respective subject field onto descriptors and inverted lists for terms occuring in a set of documents of the subject field and descriptors manually assigned to these documents. The indexing dictionary can be derived automatically from a set of manually indexed documents. An application of the network model is described, followed by an indexing example and some experimental results about the indexing performance of the network model.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

Additional Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS

  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory
          Subjects: Network problems

  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Dictionaries

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

General Terms:
Algorithms, Documentation, Experimentation, Languages, Performance