Learning Query Expansion from Association Rules Between Terms

Ahlem Bouziri, Chiraz Latiri, Eric Gaussier, Yassin Belhareth

Abstract

Query expansion technique offers an interesting solution for obtaining a complete answer to a user query while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. In this paper, we attempt to use data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. Face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results, we then build a model able to predict which association rules are to be used when expanding a query. The experiments were performed on SDA 95 collection, a data collection for information retrieval. The main observation is that the hybridization of textmining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.

References

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Paper Citation


in Harvard Style

Bouziri A., Latiri C., Gaussier E. and Belhareth Y. (2015). Learning Query Expansion from Association Rules Between Terms . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 525-530. DOI: 10.5220/0005642705250530


in Bibtex Style

@conference{kdir15,
author={Ahlem Bouziri and Chiraz Latiri and Eric Gaussier and Yassin Belhareth},
title={Learning Query Expansion from Association Rules Between Terms},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={525-530},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005642705250530},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Learning Query Expansion from Association Rules Between Terms
SN - 978-989-758-158-8
AU - Bouziri A.
AU - Latiri C.
AU - Gaussier E.
AU - Belhareth Y.
PY - 2015
SP - 525
EP - 530
DO - 10.5220/0005642705250530