A Multi-Objective Evolutionary Algorithm Approach to Learn Disjunctive and Conjunctive Topical Queries

CECCHINI, ROCÍO LUJÁN; LORENZETTI, CARLOS MARTÍN; MAGUITMAN, ANA GABRIELA

Mar del Plata, Buenos Aires, Argentina

Congreso; 38º JAIIO: Jornadas Argentinas de Informática e Investigación Operativa. ARGENTINE SYMPOSIUM ON ARTIFICIAL INTELLIGENCE 2009; 2009

SADIO - Sociedad Argentina de Informática

Topical search refers to the process of formulating queries that reflect a thematic context. A combination of machine learning and information retrieval techniques can be applied to automate this process. In this work we propose the application of single- and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. In our evaluations we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. In addition, our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand.