Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
XClust: clustering XML schemas for effective integration
Proceedings of the eleventh international conference on Information and knowledge management
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
Clio grows up: from research prototype to industrial tool
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Using Google distance to weight approximate ontology matches
Proceedings of the 16th international conference on World Wide Web
Leveraging data and structure in ontology integration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Interactive generation of integrated schemas
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
An empirical study of instance-based ontology matching
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Matching ontologies in open networked systems: techniques and applications
Journal on Data Semantics V
Matching unstructured vocabularies using a background ontology
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Discovering the semantics of relational tables through mappings
Journal on Data Semantics VII
Candidate reduction and alignment improvement techniques used in aligning ontologies
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Techniques for discovering correspondences between ontologies
International Journal of Web and Grid Services
A structure-based similarity spreading approach for ontology matching
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
NeMa: fast graph search with label similarity
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community. In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight ontology which represents the local domain knowledge, we extract a "similar" sub-ontology from the corresponding heavyweight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly calculate the influence values between concepts. In addition, we perform an extensive experiment to verify the effectiveness and efficiency of our proposed approach by using OAEI2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time.