Authors:
Ahlem Bouziri
1
;
Chiraz Latiri
2
;
Eric Gaussier
3
and
Yassin Belhareth
4
Affiliations:
1
ENSI - Manouba University and LIPAH-FST, Tunisia
;
2
ISAMM-Manouba University andLIPAH, Tunisia
;
3
Université Joseph Fourrier-Laboratoire d’Informatique de Grenoble, France
;
4
ISAMM- Manouba University, Tunisia
Keyword(s):
Query Expansion, Association Rules, Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Text and Semi-Structured Data
;
Symbolic Systems
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.
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