Combining Uncertain Outputs from Multiple Ontology Matchers
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
A new model of evaluating concept similarity
Knowledge-Based Systems
Integrating uncertainty into ontology mapping
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Ontology mapping approach based on concept dimensions
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Combining ICS semantic factor into concept similarity evaluating based on RFCA
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
What's happening in semantic web: and what FCA could have to do with it
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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The idea of the semantic web is to add machineprocessable information to web-based data in order to realize interoperability, and a key objective for semantic web services is to provide machine interpretable descriptions of web services so that other software agents can use them without having any prior 'built-in' knowledge about how to invoke them. Ontologies play a prominent role in the concept of the semantic web to provide semantic information for assisting communication among heterogeneous information repositories. As increasing numbers of ontologies are developed by diverse communities, the demand for rapid ontology mapping is arising. In this paper, a novel similarity measure method based on rough set and formal concept analysis (RFCA) is proposed to realize ontology mapping tasks. A reference concept lattice is first constructed with the combination of two normalized contexts. Rough set theory is then employed to calculate the similarity measure of the two ontology nodes. With a specified threshold, the final result of ontology mapping can be obtained. Compared with other mapping algorithms, the proposed ontology mapping method is featural and structural, and the mapping results are accurate and more confident.