Abstract
The evaluation of matching applications is becoming a major issue in the semantic web and it requires a suitable methodological approach as well as appropriate benchmarks. In particular, in order to evaluate a matching application under different experimental conditions, it is crucial to provide a test dataset characterized by a controlled variety of different heterogeneities among data that rarely occurs in real data repositories. In this paper, we propose SWING (Semantic Web INstance Generation), a disciplined approach to the semi-automatic generation of benchmarks to be used for the evaluation of matching applications. © 2011 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Ferrara, A., Montanelli, S., Noessner, J., & Stuckenschmidt, H. (2011). Benchmarking matching applications on the semantic Web. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6643 LNCS, pp. 108–122). Springer Verlag. https://doi.org/10.1007/978-3-642-21064-8_8
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