A Framework for Information Retrieval Based on Fuzzy Relations and Multiple Ontologies
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Towards a framework for trusting the automated learning of social ontologies
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Mining Fuzzy Domain Ontology Based on Concept Vector from Wikipedia Category Network
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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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.