Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Database Schema Matching Using Machine Learning with Feature Selection
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Information retrieval and machine learning for probabilistic schema matching
Proceedings of the 14th ACM international conference on Information and knowledge management
Class structures and lexical similarities of class names for ontology matching
ODBIS'05/06 Proceedings of the First and Second VLDB conference on Ontologies-based databases and information systems
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Lexical similarity based ontology mappings are useful to obtain semantic translations of database schemas across application domains. Incremental improvement of such mappings can be obtained from human inputs of ontology mapping. Manual mappings are labor intensive and need to be assisted by machine-generated mappings in a semi-automated approach. Heuristics based approaches allow multiple strategies to learn human expertise in concept mappings. Such learning improves the level of automation of the mapping process. We analyze heuristics based Bayesian learning of manual mappings to improve effectiveness of machine-generated mappings. Our results show that human based mappings contribute higher improvement in the machine-generated values of lexical similarity in comparison to those of structural similarity. The optimal weightage for structural similarity learning is inversely proportional to the complexity of given ontology graphs.