An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Ontology Matching
Falcon-AO: A practical ontology matching system
Web Semantics: Science, Services and Agents on the World Wide Web
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Semantic precision and recall for ontology alignment evaluation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
A method for recommending ontology alignment strategies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Assessing linked data mappings using network measures
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
An effective rule miner for instance matching in a web of data
Proceedings of the 21st ACM international conference on Information and knowledge management
Linked data classification: a feature-based approach
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Ontology matching is one of the key research topics in Semantic Web. In the last few years, many matching methods have been proposed to generate matches between different ontologies either automatically or semi-automatically. To select appropriate ones, users need some measures to judge whether a method can achieve the similar compliance even on one dataset without reference matches and whether such a method is reliable w.r.t. its output result along with the confidence. However, widely-used traditional measures like precision and recall fail to provide sufficient hints. In this paper, we design two novel evaluation measures to evaluate stability of matching methods and one measure to evaluate credibility of matching confidence values, which help answer the above two questions. Additionally, we carry out comparisons among several carefully selected methods systematically using our new measures. Besides, we report some interesting findings such as identifying potential defects of our subjects.