Amalgamation of Ontologies via a Statistical Approach
As ontology is subjective and varies in different domains, the amount of ontologies turns out to be huge but with poor compatibility. Mainstream method for ontology integration is mostly achieved by establishing mappings between ontologies. In this essay, the author put forward another way of ontology merging. After statistic machine learning on concept relations, the frequency of different ontologies appeared in concept relations reveals certainty factor and help to build a large-scale concept relations network including the statistic information and domain categories, so that the conceptions conveyed by different ontologies can be fused together and the merging concept space turns to be relatively objective. And the experiments results also help to demonstrate the feasibility of the ontology merging.
ontology statistic sample skewness machine learning
Peng Liu Gonghua Xu Chuang Xu Xiaoxuan Wang Xiaoying Wang
Command Automation Institute, PLA University of Science & Technology, Nanjing, China School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Department of Surveying and Geo-informatics, Tongji University, Shanghai, China
国际会议
南京
英文
856-860
2010-11-01（万方平台首次上网日期，不代表论文的发表时间）