An information retrieval model based on simple Bayesian networks

In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two versions of an extended model, which also represents direct relationships between documents. In either case the BNs are used to compute efficiently, by means of a new and exact propagation algorithm, the posterior probabilities of relevance of the documents in the collection given a query. The performance of the proposed retrieval models is tested through a series of experiments with several standard document collections. © 2003 Wiley Periodicals, Inc.

[1]  JonesK. Sparck,et al.  A probabilistic model of information retrieval , 2000 .

[2]  Luis M. de Campos,et al.  Building Bayesian network-based information retrieval systems , 2000, Proceedings 11th International Workshop on Database and Expert Systems Applications.

[3]  Alfred V. Aho,et al.  Data Structures and Algorithms , 1983 .

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

[5]  J. Huete,et al.  On the use of independence relationships for learning simplified belief networks , 1997 .

[6]  Donna K. Harman,et al.  Overview of the Sixth Text REtrieval Conference (TREC-6) , 1997, Inf. Process. Manag..

[7]  M. E. Maron,et al.  On Relevance, Probabilistic Indexing and Information Retrieval , 1960, JACM.

[8]  L. R. Rasmussen,et al.  In information retrieval: data structures and algorithms , 1992 .

[9]  Luis M. de Campos,et al.  On the use of independence relationships for learning simplified belief networks , 1997, Int. J. Intell. Syst..

[10]  Robert M. Fung,et al.  Applying Bayesian networks to information retrieval , 1995, CACM.

[11]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[12]  S. Robertson.  The probability ranking principle in IR , 1997 .

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

[14]  J. Pearl,et al.  Learning simple causal structures , 1993 .

[15]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[16]  Stephen E. Robertson,et al.  A probabilistic model of information retrieval: development and comparative experiments - Part 1 , 2000, Inf. Process. Manag..

[17]  Donna K. Harman,et al.  Overview of the Fifth Text REtrieval Conference (TREC-5) , 1996, TREC.

[18]  Donna K. Harman,et al.  Overview of the Ninth Text REtrieval Conference (TREC-9) , 2000, TREC.

[19]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.