Mining fuzzy ontology for fuzzy granular IR systems
Raymond Y.K. Lau, Long Song · 2012
Similarity-based and popularity-based information retrieval (IR) models have been widely used by general Internet search engines. However, there are weaknesses of these models for supporting domain-specific IR. Grounded on the work in granular computing, we propose the notion of semantic information granulation estimated with respect to a fuzzy domain ontology to support domain-specific IR. The main contributions of this paper is the illustration of the design and development of a fuzzy granular IR system which can take into account two orthogonal dimensions, such as “similarity” and “granularity” to improve the effectiveness of domain-specific IR. In particular, the proposed fuzzy granular IR system is underpinned by a computational method for automated fuzzy ontology mining. Based on standard benchmark document collection, the results of our experiments confirm that the proposed fuzzy granular IR system outperforms a classical similarity-based (i.e, the vector space model) IR system for domain specific IR. Our research opens the door to the applications of granular computing and fuzzy ontology mining to enhance Internet search engines.