Integration of probabilistic fact and text retrieval

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.

[1]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[2]  Hans-Peter Kriegel,et al.  Geometry-based similarity retrieval of rotational parts , 1989, Proceedings. Second International Conference on Data and Knowledge Systems for Manufacturing and Engineering.

[3]  Gerard Salton,et al.  The SMART Retrieval System , 1971 .

[4]  W. Bruce Croft,et al.  Inference networks for document retrieval , 1989, SIGIR '90.

[5]  Christoph Schwarz.  Automatic syntactic analysis of free text , 1990, J. Am. Soc. Inf. Sci..

[6]  Vijay V. Raghavan,et al.  A Unified Architecture for the Integration of Data Base Management and Information Retrieval Systems , 1986, IFIP Congress.

[7]  Pasquale Savino,et al.  Retrieval of Multimedia Documents by Imprecise Query Specification , 1990, EDBT.

[8]  Christoph Schwarz,et al.  Automatic syntactic analysis of free text , 1990, J. Am. Soc. Inf. Sci..

[9]  Stephen E. Robertson,et al.  Probabilistic models of indexing and searching , 1980, SIGIR '80.

[10]  Norbert Fuhr,et al.  Models for retrieval with probabilistic indexing , 1989, Inf. Process. Manag..

[11]  Vijay V. Raghavan,et al.  Design of an Integrated Information Retrieval/Database Management System , 1990, IEEE Trans. Knowl. Data Eng..

[12]  Yiyu Yao,et al.  Query formulation in linear retrieval models , 1990, J. Am. Soc. Inf. Sci..

[13]  Henri Prade,et al.  Generalizing Database Relational Algebra for the Treatment of Incomplete/Uncertain Information and Vague Queries , 1984, Inf. Sci..

[14]  Clement T. Yu,et al.  Precision Weighting—An Effective Automatic Indexing Method , 1976, J. ACM.

[15]  D. Tsichritzis,et al.  SaTellite: a visualization and navigation tool for hypermedia , 1990 .

[16]  Gerard Salton,et al.  Research and Development in Information Retrieval , 1982, Lecture Notes in Computer Science.

[17]  William S. Cooper,et al.  Some inconsistencies and misnomers in probabilistic information retrieval , 1991, SIGIR '91.

[18]  Norbert Fuhr,et al.  Combining model-oriented and description-oriented approaches for probabilistic indexing , 1991, SIGIR '91.

[19]  Dennis Tsichritzis,et al.  Advances in Database Technology — EDBT '90 , 1990, Lecture Notes in Computer Science.

[20]  Andrew K. C. Wong,et al.  Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  W. Bruce Croft,et al.  Interactive retrieval office documents , 1988, COIS.

[22]  W. B. Croft,et al.  Interactive retrieval office documents , 1988 .

[23]  Norbert Fuhr,et al.  A Probabilistic Framework for Vague Queries and Imprecise Information in Databases , 1990, VLDB.

[24]  Chris Buckley,et al.  Implementation of the SMART Information Retrieval System , 1985 .

[25]  Norbert Fuhr,et al.  AIR/X - A rule-based multistage indexing system for Iarge subject fields , 1991, RIAO.

[26]  Chris Buckley,et al.  A probabilistic learning approach for document indexing , 1991, TOIS.

[27]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[28]  Amihai Motro,et al.  VAGUE: a user interface to relational databases that permits vague queries , 1988, TOIS.

[29]  Abraham Kandel,et al.  Implementing Imprecision in Information Systems , 1985, Inf. Sci..