Automatic Detection and Processing of Attributes Inconsistency for Fuzzy Ontologies Merging

Yonghong Luo, Zhou Yu, Yanhao Zhuang, Zhaopeng Zheng


Semantic fusion of multiple data sources and semantic interoperability between heterogeneous systems in distributed environment can be implemented through integrating multiple fuzzy local ontologies. However, ontology merging is one of the valid ways for ontology integration. In order to solve the problem of attributes inconsistency for concept mapping in fuzzy ontology merging system, we present an automatic detection algorithm of inconsistency for the range, number and membership grade of attributes between mapping concepts, and adopt corresponding processing strategy during the fuzzy ontologies merging according to the different types of attributes inconsistency. Experiment results show that with regard to merging accuracy, the fuzzy ontology merging system in which the automatic detection algorithm and processing strategy of attributes inconsistency is embedded is better than those traditional ontology merging systems like GLUE, PROMPT and Chimaera.




Fuzzy Ontology; Ontology Merging; Automatic Detection; Attributes Inconsistency; Membership Grade

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