Exploiting Morphological Query Structure Using Genetic Optimisation
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Structure of morphologically expanded queries: A genetic algorithm approach
Data & Knowledge Engineering
Query clauses and term independence
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Shape pattern matching: A tool to cluster unstructured text documents
Journal of Computational Methods in Sciences and Engineering - Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2009
Aggregating semantic concepts for event representation in lifelogging
Proceedings of the International Workshop on Semantic Web Information Management
Using genetic algorithms for query reformulation
FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access
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The vector space model is a mathematical-based model that represents terms, documents and queries by vectors and provides a ranking. In this model, the subspace of interest is formed by a set of pairwaise orthogonal term vectors, indicating that terms are mutually independent. However, this is a simplification that doesn't correspond to the reality. Based on this scenery, we present, in this work, an extension to the vector space model to take into account the correlation between terms. In the proposed model, term vectors are rotated in space geometrically reflecting the dependence semantics among terms. We rotate terms based on a data mining technique called association rules. The retrieval effectiveness of the proposed model is evaluated and the results shows that our model improves in average precision, relative to the standard vector space model, for all collections evaluated, leading to a gain up to 31%.