Abstract
The semantic web provides access to an increasing number of linked datasets expressed in RDF. One feature of these datasets is that they are not constrained by a schema. Such schema could be very useful as it helps users understand the structure of the entities and can ease the exploitation of the dataset. Several works have proposed clustering-based schema discovery approaches which provide good quality schema, but their ability to process very large RDF datasets is still a challenge. In this work, we address the problem of automatic schema discovery, focusing on scalability issues. We introduce an approach, relying on a scalable density-based clustering algorithm, which provides the classes composing the schema of a large dataset. We propose a novel distribution method which splits the initial dataset into subsets, and we provide a scalable design of our algorithm to process these subsets efficiently in parallel. We present a thorough experimental evaluation showing the effectiveness of our proposal.
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Notes
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RDF: https://www.w3.org/RDF/.
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OWL: https://www.w3.org/OWL/.
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Silk: http://silkframework.org/.
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Bouhamoum, R., Kedad, Z., Lopes, S. (2020). Scalable Schema Discovery for RDF Data. In: Hameurlain, A., Tjoa, A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVI. Lecture Notes in Computer Science(), vol 12410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62386-2_4
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