Multi-objective Query Optimization Using Topic Ontologies

Abstract

Formulating search queries based on a thematic context is a challenging problem due to the large number of combinations in which terms can be used to reflect the topic of interest. This paper presents a novel approach to learn topical queries that simultaneously satisfy multiple retrieval objectives. The proposed method consists in using a topic ontology to train an Evolutionary Algorithm that incrementally moves a population of queries towards the proposed objectives. We present an analysis of different singleand multi-objective strategies, discuss their strengths and limitations and test the most promising strategies on a large set of labeled Web pages. Our evaluations indicate that the tested strategies that apply multi-objective Evolutionary Algorithms are significantly superior to a baseline approach that attempts to generate queries directly from a topic description.

DOI: 10.1007/978-3-642-04957-6_13

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Cite this paper

@inproceedings{Cecchini2009MultiobjectiveQO, title={Multi-objective Query Optimization Using Topic Ontologies}, author={Roc{\'i}o L. Cecchini and Carlos M. Lorenzetti and Ana Gabriela Maguitman}, booktitle={FQAS}, year={2009} }