An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Ontology Matching
Machine Learning Approach for Ontology Mapping Using Multiple Concept Similarity Measures
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Integrating multiple internet directories by instance-based learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Using pseudo feedback to improve cross-lingual ontology mapping
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A configurable translation-based cross-lingual ontology mapping system to adjust mapping outcomes
Web Semantics: Science, Services and Agents on the World Wide Web
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This paper presents an analysis of similarity measures for identifying ontology mapping. Using discriminant analysis, we investigated forty-eight similarity measures such as string matching and knowledge based similarities that have been used in previous systems. As a result, we extracted twenty-two effective similarity measures for identifying ontology mapping out of forty-eight possible similarity measures. The extracted measures vary widely in the type in similarity.