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
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
AI Magazine - Special issue on semantic integration
Ontology Matching
STBenchmark: towards a benchmark for mapping systems
Proceedings of the VLDB Endowment
Schema Matching and Mapping
A survey of schema-based matching approaches
Journal on Data Semantics IV
Non-binary evaluation for schema matching
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Toward a business model reference for interoperability services
Computers in Industry
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Generating new knowledge from scientific databases, fusioning products information of business companies or computing an overlap between various data collections are a few examples of applications that require data integration. A crucial step during this integration process is the discovery of correspondences between the data sources, and the evaluation of their quality. For this purpose, the overall metric has been designed to compute the post-match effort, but it suffers from major drawbacks. Thus, we present in this paper two related metrics to compute this effort. The former is called post-match effort, i.e., the amount of work that the user must provide to correct the correspondences that have been discovered by the tool. The latter enables the measurement of human-spared resources, i.e., the rate of automation that has been gained by using a matching tool.