Multi-objective Query Optimization Using Topic Ontologies

CECCHINI ROCIO LUJÁN; LORENZETTI CARLOS MARTÍN; MAGUITMAN ANA GABRIELA

LECTURE NOTES IN COMPUTER SCIENCE

Springer

Lugar: Heidelberg; Año: 2009 vol. 5822 p. 145 - 156

0302-9743

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 single- and 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.