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Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Automating RDF Dataset Transformation and Enrichment

Authors : Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo, Jens Lehmann

Published in: The Semantic Web. Latest Advances and New Domains

Publisher: Springer International Publishing

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Abstract

With the adoption of RDF across several domains, come growing requirements pertaining to the completeness and quality of RDF datasets. Currently, this problem is most commonly addressed by manually devising means of enriching an input dataset. The few tools that aim at supporting this endeavour usually focus on supporting the manual definition of enrichment pipelines. In this paper, we present a supervised learning approach based on a refinement operator for enriching RDF datasets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against eight manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples.

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previous chapter HAWK – Hybrid Question Answering Using Linked Data
next chapter Semi-supervised Instance Matching Using Boosted Classifiers
Footnotes
4
DrugBank is the Linked Data version of the DrugBank database, which is a repository of almost 5000 FDA-approved small molecule and biotech drugs, for RDF dump see http://​wifo5-03.​informatik.​uni-mannheim.​de/​drugbank/​drugbank_​dump.​nt.​bz2.
 
5
See http://​datahub.​io/​dataset/​fu-berlin-drugbank for complete list of linked dataset with DrugBank.
 
9
Jamendo contains a large collection of music related information about artists and recordings, for RDF dump see http://​moustaki.​org/​resources/​jamendo-rdf.​tar.​gz.
 
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Metadata
Title
Automating RDF Dataset Transformation and Enrichment
Authors
Mohamed Ahmed Sherif
Axel-Cyrille Ngonga Ngomo
Jens Lehmann
Copyright Year
2015
Book
The Semantic Web. Latest Advances and New Domains

Print ISBN: 978-3-319-18817-1

Electronic ISBN: 978-3-319-18818-8

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-18818-8_23