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Learning Sparse Overcomplete Word Vectors Without Intermediate Dense Representations Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Mining Schema Knowledge from Linked Data on the Web

Authors : Razieh Mehri, Petko Valtchev

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

Datasets on the Web of Data (WoD) are often published without a precise schema which may discourage their reuse. Methods for schema acquisition from linked data have been proposed that mainly exploit the regularities in property and/or value distributions in resources to discover potentially useful classes as homogeneous clusters. Yet the crucial task of interpreting and naming the discovered classes is left to the human analyst. We prone a more holistic approach to schema discovery that, beside clustering, assists the analyst by suggesting plausible names for clusters. In doing that we: (1) rely on concept analysis for class discovery from linked data and (2) exploit known DBpedia types and shared properties to form candidate names. An evaluation of our approach with a dataset from the WoD showed it performs well.

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Metadata
Title
Mining Schema Knowledge from Linked Data on the Web
Authors
Razieh Mehri
Petko Valtchev
Copyright Year
2017
Book
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63558-3_22

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