Domain ontology learning and consistency checking based on TSC approach and racer

Authors:
Xi Bai;Jigui Sun;Zehai Li;Xianrui Lu
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Chan ...;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Chan ...;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Chan ...;College of Mathematics, Jilin University, Changchun, China
Venue:
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
Year:
2007

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Abstract

Building domain ontology is time consuming and tedious since it is usually done by domain experts and knowledge engineers manually. This paper proposes a two-stage clustering approach for semiautomatically building ontologies from the Chinese-document corpus based on SOM neural network and agglomerative hierarchical clustering and automatically checking the ontology consistency. Chinese lexical analysis and XML Path Language(XPath) are used in the process of extracting resources from Web documents. In our experiment, this twostage clustering approach is used for building an automobile ontology. Experimental results and the comparison with the more conventional ontology-generation method are presented and discussed, indicating the high performance of our approach. A Racer-based consistency-checking method of reasoning is presented in this paper. An ontology evolution method and performance evaluation are also given.