Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Interactive document retrieval with relational learning
Proceedings of the 2001 ACM symposium on Applied computing
Modern Information Retrieval
A Semiotic Model of Communication and Its Implications for the Digital City Development
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Learning filtering rulesets for ranking refinement in relevance feedback
Knowledge-Based Systems
Adopting ontologies and rules in web searching services
CIS'04 Proceedings of the First international conference on Computational and Information Science
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This paper describes a system for collecting Web pages that are relevant to a particular topic through an interactive approach. Indicated some relevant pages by a user, this system automatically constructs a set of rules to find new relevant pages. The purpose of the system is to reduce users' browsing cost by filtering non-relevant pages automatically. Such an approach can be useful when users do not know how to describe their requirements to search engines. We describe the representation and the learning algorithm, and also show the experiments comparing its performance with a search engine.