Integration of probabilistic fact and text retrieval

ABSTRACT

In this paper, a model for combining text and fact retrieval is described. A query is a set of conditions, where a single condition is either a text or fact condition. Fact conditions can be interpreted as being vague, thus leading to nonbinary weights for fact conditions with respect to database objects. For text conditions, we use descriptions of the occurence of terms in documents instead of precomputed indexing weights, thus treating terms similar to attributes. Probabilistic indexing weights for conditions are computed by introducing the notion of correctness (or acceptability) of a condition w.r.t. an object. These indexing weights are used in retrieval for a probabilistic ranking of objects based on the retrieval for a probabilistic ranking of objects based on the retrieval-with-probabilistic-indexing (RPI) model, for which a new derivation is given here.

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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:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.1 Document and Text Editing

General Terms:
Algorithms, Languages