Mining Fuzzy Domain Ontology from Textual Databases

Abstract

Ontology plays an essential role in the formalization of common information (e.g., products, services, relationships of businesses) for effective human-computer interactions. However, engineering of these ontologies turns out to be very labor intensive and time consuming. Although some text mining methods have been proposed for automatic or semi-automatic discovery of crisp ontologies, the robustness, accuracy, and computational efficiency of these methods need to be improved to support large scale ontology construction for real-world applications. This paper illustrates a novel fuzzy domain ontology mining algorithm for supporting real-world ontology engineering. In particular, contextual information of the knowledge sources is exploited for the extraction of high quality domain ontologies and the uncertainty embedded in the knowledge sources is modeled based on the notion of fuzzy sets. Empirical studies have confirmed that the proposed method can discover high quality fuzzy domain ontology which leads to significant improvement in information retrieval performance.