Mining Fuzzy Domain Ontology from Textual Databases

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 156 - 162
• Main Authors: Lau, Raymond Y. K., Li, Yuefeng, Xu, Yue
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Probabilistic reasoning; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Vagueness and fuzzy logic; Information systems > Data management systems > Database management system engines; Information systems > Information retrieval; Information systems > Information retrieval > Evaluation of retrieval results; Information systems > Information systems applications > Data mining; Fuzzy Sets; Semantic Web; Text Mining; Fuzzy Domain Ontology
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.76

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.