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2020 | OriginalPaper | Chapter

Scalable Schema Discovery for RDF Data

Authors : Redouane Bouhamoum, Zoubida Kedad, Stéphane Lopes

Published in: Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVI

Publisher: Springer Berlin Heidelberg

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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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Title: Scalable Schema Discovery for RDF Data
Authors: Redouane Bouhamoum
Zoubida Kedad
Stéphane Lopes
Publisher: Springer Berlin Heidelberg
Book: Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVI
Print ISBN: 978-3-662-62385-5

Electronic ISBN: 978-3-662-62386-2

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-662-62386-2_4

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