research-article

Graph-based ontology analysis in the linked open data

Authors:

Ryutaro IchiseAuthors Info & Claims

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

September 2012

Pages 56 - 63

https://doi.org/10.1145/2362499.2362508

Published: 05 September 2012 Publication History

Abstract

The Linked Open Data (LOD) includes over 31 billion Resource Description Framework (RDF) triples interlinked by around 504 million SameAs links (as of September 2011). The data sets of the LOD use different ontologies to describe instances, that cause the ontology heterogeneity problem. Dealing with the heterogeneous ontologies is a challenging problem and it is time-consuming to manually learn big ontologies in the LOD. The heterogeneity of ontologies in the LOD can be reduced by automatically integrating related ontology classes and properties, which can be retrieved from interlinked instances. The interlinked instances can be represented as an undirected graph, from which we can discover the characteristics of instances and retrieve related ontology classes and properties that are important for linking instances. In this paper, we retrieve graph patterns from several linked data sets and perform ontology alignment methods on each graph pattern to identify related ontology classes and properties from the data sets. We successfully integrate various ontologies, analyze the characteristics of interlinked instances, and detect mistaken properties in the real data sets. Furthermore, our approach solves the ontology heterogeneity problem and helps Semantic Web application developers easily query on various data sets with the integrated ontology.

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Cited By

Gunaratna KLalithsena SSheth A(2018)Alignment and dataset identification of linked data in Semantic WebWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11214:2(139-151)Online publication date: 14-Dec-2018
https://dl.acm.org/doi/10.1002/widm.1121
Zhang ZGentile ABlomqvist EAugenstein ICiravegna F(2016)An unsupervised data-driven method to discover equivalent relations in large Linked DatasetsSemantic Web10.3233/SW-1501938:2(197-223)Online publication date: 6-Dec-2016
https://doi.org/10.3233/SW-150193
RAHOMAN MICHISE R(2016)Automatic Erroneous Data Detection over Type-Annotated Linked DataIEICE Transactions on Information and Systems10.1587/transinf.2015DAP0022E99.D:4(969-978)Online publication date: 2016
https://doi.org/10.1587/transinf.2015DAP0022
Show More Cited By

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cover image ACM Other conferences

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

September 2012

215 pages

ISBN:9781450311120

DOI:10.1145/2362499

Editors:
Harald Sack
Hasso-Plattner-Institut für Softwaresystemtechnik GmbH
,
Tassilo Pellegrini
University of Applied Sciences St. Pölten
,
Program Chairs:
Valentina Presutti
Institute for Cognitive Science and Technology, Rome - Italy
,
H. Sofia Pinto
Technical University of Lisbon - Portugal

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

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I-SEMANTICS '12

I-SEMANTICS '12: I-SEMANTICS 2012 - 8th International Conference on Semantic Systems

September 5 - 7, 2012

Graz, Austria

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Cited By

Gunaratna KLalithsena SSheth A(2018)Alignment and dataset identification of linked data in Semantic WebWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11214:2(139-151)Online publication date: 14-Dec-2018
https://dl.acm.org/doi/10.1002/widm.1121
Zhang ZGentile ABlomqvist EAugenstein ICiravegna F(2016)An unsupervised data-driven method to discover equivalent relations in large Linked DatasetsSemantic Web10.3233/SW-1501938:2(197-223)Online publication date: 6-Dec-2016
https://doi.org/10.3233/SW-150193
RAHOMAN MICHISE R(2016)Automatic Erroneous Data Detection over Type-Annotated Linked DataIEICE Transactions on Information and Systems10.1587/transinf.2015DAP0022E99.D:4(969-978)Online publication date: 2016
https://doi.org/10.1587/transinf.2015DAP0022
Holub MProksa OBieliková M(2015)Detecting Identical Entities in the Semantic Web DataProceedings of the 41st International Conference on SOFSEM 2015: Theory and Practice of Computer Science - Volume 893910.1007/978-3-662-46078-8_43(519-530)Online publication date: 24-Jan-2015
https://dl.acm.org/doi/10.1007/978-3-662-46078-8_43
Zhao LIchise R(2014)Ontology Integration for Linked DataJournal on Data Semantics10.1007/s13740-014-0041-93:4(237-254)Online publication date: 25-May-2014
https://doi.org/10.1007/s13740-014-0041-9
ZHAO LICHISE R(2013)Integrating Ontologies Using Ontology Learning ApproachIEICE Transactions on Information and Systems10.1587/transinf.E96.D.40E96.D:1(40-50)Online publication date: 2013
https://doi.org/10.1587/transinf.E96.D.40
Gunaratna KThirunarayan KJain PSheth AWijeratne S(2013)A statistical and schema independent approach to identify equivalent properties on linked dataProceedings of the 9th International Conference on Semantic Systems10.1145/2506182.2506187(33-40)Online publication date: 4-Sep-2013
https://dl.acm.org/doi/10.1145/2506182.2506187
Zhao LIchise R(2013)Instance-Based Ontological Knowledge AcquisitionThe Semantic Web: Semantics and Big Data10.1007/978-3-642-38288-8_11(155-169)Online publication date: 2013
https://doi.org/10.1007/978-3-642-38288-8_11

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