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Towards an effective automatic query expansion process using an association rule mining approach

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Abstract

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.

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Notes

  1. By analogy to the itemset terminology used in data mining for a set of items.

  2. The AMARYLLIS project is initiated by INIST-CNRS and co-funded by AUPELF-UREF. Its goal is to evaluate French Text retrieval systems.

  3. The Cross-Language Evaluation Forum (CLEF) promotes multilingual information access. It offers benchmark collection data for evaluating IR systems. The associated website is: http://www.clef-campaign.org/.

  4. In this paper, we denote by |X| the cardinality of the set X.

  5. In the remainder, T 1 \(\stackrel{c}{\Rightarrow}\) T 2 indicates that the rule T 1 \({\Rightarrow}\) T 2 has a value of confidence equal to c.

  6. The rule T \(\Rightarrow\) ∅ is usually considered as not informative.

  7. Available at: http://www.adrem.ua.ac.be/~goethals/software/.

  8. Distributed by the Synapse Development Corporation.

  9. maxsupp means that the termset must occur at most below than this user-defined threshold.

  10. Test datasets are available at: http://fimi.cs.helsinki.fi/data.

  11. Freely available at: http://sourceforge.net/projects/lemur/, while the information about this toolkit is available at: http://www.lemurproject.org/.

  12. idf is the acronym of inverted document frequency.

  13. By baseline, we refer to the baselines using tf×idf, BM25tf and OKAPI BM25 weighting schemes.

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Acknowledgements

We would like to thank the anonymous reviewers for their helpful comments and suggestions. We are also grateful to the Evaluations and Language resources Distribution Agency (ELDA) which kindly provided us the LE Monde 94 and ATS 94 document collections of the CLEF 2003 campaign.

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  1. URPAH Team, Computer Sciences Department, Faculty of Sciences of Tunis, El Manar University, Tunis, Tunisia

    Chiraz Latiri, Hatem Haddad & Tarek Hamrouni

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  1. Chiraz Latiri
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  3. Tarek Hamrouni
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Correspondence to Tarek Hamrouni.

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Latiri, C., Haddad, H. & Hamrouni, T. Towards an effective automatic query expansion process using an association rule mining approach. J Intell Inf Syst 39, 209–247 (2012). https://doi.org/10.1007/s10844-011-0189-9

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