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Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results
Carlos Iván Chesñevar*, and Ana Gabriela Maguitman**
*Department of Computer Science, University of Lleida, 25001 Lleida, Spain
**Computer Science Department, Indiana University, Bloomington, IN 47405-7104, USA
Received:November 18, 2004Accepted:November 22, 2004Published:January 20, 2005
Keywords:recommender systems, defeasible argumentation, decision support systems
Abstract
Several techniques for improving web search have been developed over the last years. Most existing approaches are still limited, mainly due to the absence of qualitative criteria for ranking results and insensitivity to user preferences for guiding the search. At the same time, defeasible argumentation evolved as a successful approach in AI to model commonsense qualitative reasoning with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents ARGUENET, a recommender system that classifies search results according to preference criteria declaratively specified by the user. The proposed approach integrates a traditional web search engine with a defeasible argumentation framework.
Cite this article as:C. Chesñevar and A. Maguitman, “Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results,” J. Adv. Comput. Intell. Intell. Inform., Vol.9 No.1, pp. 53-60, 2005.Data files:
