Automated query learning with Wikipedia and genetic programming

  • Authors:

  • Pekka Malo;Pyry Siitari;Ankur Sinha

  • Affiliations:

  • Aalto University, School of Economics, P.O. Box 21210, FI-00076 Aalto, Finland;Aalto University, School of Economics, P.O. Box 21210, FI-00076 Aalto, Finland;Aalto University, School of Economics, P.O. Box 21210, FI-00076 Aalto, Finland

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2013

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Abstract

Most of the existing information retrieval systems are based on bag-of-words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents Wikipedia-based Evolutionary Semantics (Wiki-ES) framework for generating concept based queries using a set of relevance statements provided by the user. The query learning is handled by a co-evolving genetic programming procedure. To evaluate the proposed framework, the system is compared to a bag-of-words based genetic programming framework as well as to a number of alternative document filtering techniques. The results obtained using Reuters newswire documents are encouraging. In particular, the injection of Wikipedia semantics into a GP-algorithm leads to improvement in average recall and precision, when compared to a similar system without human knowledge. A further comparison against other document filtering frameworks suggests that the proposed GP-method also performs well when compared with systems that do not rely on query-expression learning.