Incorporating different search models into one document retrieval system

Many effective search strategies derived from different models are available for document retrieval systems. However, it does not appear that there is a single most effective strategy. Instead, different strategies perform optimally under different conditions. This paper outlines the design of an adaptive document retrieval system that chooses the best search strategy for a particular situation and user. In order to be able to support a variety of search strategies, a general network representation of the documents and terms in the database is proposed. This network representation leads to efficient methods of generating and using document and term classifications.One of the most desirable features of an adaptive system would be the ability to learn from experience. A method of incorporating this learning ability into the system is described. The adaptive control strategy for choosing search strategies enables the system to base its actions on a number of factors, including a model of the current user.Finally, some ideas for a flexible interface for casual users are suggested. Part of this interface is the heuristic search, which is used when searches based on formal models have failed. The heuristic search provides a browsing capability for the user.

[1]  Stephen E. Robertson,et al.  Relevance weighting of search terms , 1976, J. Am. Soc. Inf. Sci..

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

[3]  Van Rijsbergen,et al.  A theoretical basis for the use of co-occurence data in information retrieval , 1977 .

[4]  Harold Borko,et al.  Automatic indexing , 1981, ACM '81.

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

[6]  W. Bruce Croft.  A model of cluster searching bases on classification , 1980, Inf. Syst..

[7]  W. Bruce Croft.  Document representation in probabilistic models of information retrieval , 1981, J. Am. Soc. Inf. Sci..

[8]  C. J. van Rijsbergen,et al.  An Evaluation of feedback in Document Retrieval using Co‐Occurrence Data , 1978, J. Documentation.

[9]  Gerard Salton,et al.  Automatic Information Organization And Retrieval , 1968 .

[10]  W. Bruce Croft.  On the implementation of some models of document retrieval , 1979, SIGIR 1979.

[11]  W. Bruce Croft,et al.  Using Probabilistic Models of Document Retrieval without Relevance Information , 1979, J. Documentation.

[12]  Clement T. Yu,et al.  Analysis of Effectiveness of Retrieval in Clustered Files , 1977, JACM.

[13]  Elaine Rich,et al.  Building and Exploiting User Models , 1979, IJCAI.

[14]  Robert N. Oddy,et al.  INFORMATION RETRIEVAL THROUGH MAN‐MACHINE DIALOGUE , 1977 .

[15]  W. Bruce Croft.  Clustering large files of documents using the single-link method , 1977, J. Am. Soc. Inf. Sci..