Paper:
Language: English:

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, 2004

Accepted: November 22, 2004

Keywords: recommender systems, defeasible argumentation, decision support systems

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.9, No.1 pp. 53-60, 2005

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.

[Notice]
* "Preview" is the first 2 pages of the article. You don't need the registration.
* To read the PDF file you will then need to download and install the Adobe Reader.
Adobe Reader is free and available for download here: