Abstract
The increasing size and the widespread use of XML data and different types of ontologies result in the big challenge of how to integrate these data. A critical step towards building this integration is to identify and discover semantically corresponding elements across heterogeneous data sets. This identification process becomes more and more challenging when dealing with large schemas and ontologies. Clustering-based matching is a great step towards more significant reduction of the search space and thus improving the matching efficiency. However, current methods used to identify similar clusters depend on literally matching terms. To keep high matching quality along with high matching efficiency, hidden semantic relationships among clusters’ elements should be discovered. To this end, in this paper, we propose a Latent Semantic Indexing-based approach that allows retrieving the conceptual meaning between clusters. The experimental evaluations reveal that the proposed approach permits encouraging and significant improvements towards building large-scale matching approaches.
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XML Schema - Data Types Quick Reference, http://www.xml.dvint.com/.
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Acknowledgments
This paper is a revised and extended version of the paper presented in [26]. A. Algergawy partially worked on this paper while at Magdeburg University.
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Algergawy, A., Moawed, S., Sarhan, A., Eldosouky, A., Saake, G. (2014). Improving Clustering-Based Schema Matching Using Latent Semantic Indexing. In: Hameurlain, A., et al. Transactions on Large-Scale Data- and Knowledge-Centered Systems XV. Lecture Notes in Computer Science(), vol 8920. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45761-0_4
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