Data & Knowledge Engineering
Heterogeneous database integration in biomedicine
Computers and Biomedical Research
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
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering Direct and Indirect Matches for Schema Elements
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
Semi-Automatic, Semantic Discovery of Properties from Database Schemes
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Industrial-strength schema matching
ACM SIGMOD Record
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Schema Matching Using Duplicates
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
Poster Session: An Indexing Structure for Automatic Schema Matching
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
An experiment on the matching and reuse of XML schemas
ICWE'05 Proceedings of the 5th international conference on Web Engineering
MDSM: Microarray database schema matching using the Hungarian method
Information Sciences: an International Journal
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Database integration aims at providing a uniform and consistent view called global schema, over a set of autonomous and heterogeneous data sources, so that data residing in different sources can be accessed as if it was in a single schema. The integration of data sources can be performed in two steps, a matching and a data transformation step. Schema matching, the focus of this paper, is a fundamental operation in the manipulation of schema in formatting match, which takes two schemas that correspond semantically to each other. Manually specifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process, which is a growing problem given the rapidly increasing number of data sources to integrate. As systems become able to handle more complex databases and applications such as biomedical databases schemas, their schemas become large, further increasing the number of matches to be performed. Several solutions in solving the issues of schema matching have been proposed. However, these solutions are still limited as (i) they do not explore most of the available information related to schemas, (ii) the approaches rely strictly on the assumption that the schemas to be matched are from the same application domain, and (iii) the approaches either match schemas by comparing the strings of the elements' names or by checking if those names are synonyms. This paper addresses the above limitations by proposing a model for matching heterogeneous relational biomedical databases' schemas that further improves the results of the integration.