Combining weights with fuzziness for intelligent semantic web search

Intelligent retrieval for best satisfying users search intensions still remains a challenging problem due to the inherent complexity of real-world semantic web applications. Usually, a search request contains not only vagueness or imprecision, but also personalized information goals. This paper presents a novel approach which formulates one’s search request through tightly combining fuzziness together with the user’s subjective weighting importance over multiple search properties. A special ranking mechanism based on the weighed fuzzy query representation is proposed. The ranking method generates a set of “degree of relevance” – an overall score which reflects both fuzzy predicates and the user’s personalized preferences in the search request. Moreover, the ranking method is general and unique rather than arbitrary. Hence, search results shall be properly ordered in terms of their relevance with respect to best matching the search intension. The experimental results show that our approach can effectively capture users information goals and produce much better search results than existing approaches.