Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
A unifying semantic distance model for determining the similarity of attribute values
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Semi-Automatic, Semantic Discovery of Properties from Database Schemes
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A model for matching and integrating heterogeneous relational biomedical databases schemas
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Integrating schemas of heterogeneous relational databases through schema matching
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
A framework for schema matcher composition
WSEAS Transactions on Computers
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Current microarray databases use different terminologies and structures and thereby limit the sharing of data and collating of results between laboratories. Consequently, an effective integrated microarray data model is required. One important process to develop such an integrated database is schema matching. In this paper, we propose an effective schema matching approach called MDSM, to syntactically and semantically map attributes of different microarray schemas. The contribution from this work will be used later to create microarray global schemas. Since microarray data is complex, we use microarray ontology to improve the measuring accuracy of the similarity between attributes. The similarity relations can be represented as weighted bipartite graphs. We determine the best schema matching by computing the optimal matching in a bipartite graph using the Hungarian optimisation method. Experimental results show that our schema matching approach is effective and flexible to use in different kinds of database models such as; database schema, XML schema, and web site map. Finally, a case study on an existing public microarray schema is carried out using the proposed method.