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Bayesian network based business information retrieval model

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Abstract

The quality of business information can significantly affect the operation level of enterprise. This paper analyses the problem of business information retrieval (BIR). A Bayesian Network Based business information retrieval model (BN-BIRM) is proposed by means of Bayesian network (BN) and information retrieval (IR) theory and a method for query adaptation is presented. In this model the customized query requirement of enterprise (CQR) is expressed in terms of the predefined illustrative documents related to business domain. The similarities between the documents and the query are evaluated with the conditional probabilities among the nodes in the BN. In the experiments, BN-BIRM is compared with the Belief Network model based on vector space model (VSM) ranking strategy and the Inference Network model based on TF-IDF ranking strategy. The experimental results show that BN-BIRM is effective for collecting business information on a large scale.

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Authors and Affiliations

  1. Department of System Engineering, School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110004, People’s Republic of China

    Zheng Wang, Qing Wang & Ding-Wei Wang

Authors
  1. Zheng Wang
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  2. Qing Wang
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  3. Ding-Wei Wang
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Corresponding author

Correspondence to Zheng Wang.

Additional information

This work was partially supported by Key Project of National Natural Science Foundation, grant 70431003, Innovative Research Team Project of National Natural Science Foundation, grant 60521003, and National Science Supporting plan, grant 2006BAH02A09 of People’s Republic of China.

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Wang, Z., Wang, Q. & Wang, DW. Bayesian network based business information retrieval model. Knowl Inf Syst 20, 63–79 (2009). https://doi.org/10.1007/s10115-008-0151-5

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  • DOI: https://doi.org/10.1007/s10115-008-0151-5

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