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Online Learning of a Weighted Selective Naive Bayes Classifier with Non-convex Optimization Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Ontology Alignment Using Web Linked Ontologies as Background Knowledge

Authors : Thomas Hecht, Patrice Buche, Juliette Dibie, Liliana Ibanescu, Cassia Trojahn dos Santos

Published in: Advances in Knowledge Discovery and Management

Publisher: Springer International Publishing

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Abstract

This paper proposes an ontology matching method for aligning a source ontology with target ontologies already published and linked on the Linked Open Data (LOD) cloud. This method relies on the refinement of a set of input alignments generated by existing ontology matching methods. Since the ontologies to be aligned can be expressed in several representation languages with different levels of expressiveness and the existing ontology matching methods can only be applied to some representation languages, the first step of our method consists in applying existing matching methods to as many ontology variants as possible. We then propose to apply two main strategies to refine the initial alignment set: the removal of different kinds of ambiguities between correspondences and the use of the links published on the LOD. We illustrate our proposal in the field of life sciences and environment.

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Footnotes
15
Since ambiguous correspondences according to type 1 produce only noises, the evaluation of the best recall is done considering \(\overset{agr*}{C}\) and not \(\overset{agr}{C}\).
 
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Metadata
Title
Ontology Alignment Using Web Linked Ontologies as Background Knowledge
Authors
Thomas Hecht
Patrice Buche
Juliette Dibie
Liliana Ibanescu
Cassia Trojahn dos Santos
Copyright Year
2017
Book
Advances in Knowledge Discovery and Management

Print ISBN: 978-3-319-45762-8

Electronic ISBN: 978-3-319-45763-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-45763-5_11

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