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On the Move to Meaningful Internet Systems: OTM 2019 ConferencesConfederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings
Type of publication
Peer-reviewed
Publikationsform
Proceedings (peer-reviewed)
Author
Mendes de Farias Tarcisio, Stockinger Kurt, Dessimoz Christophe,
Project
Bio-SODA: Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data
Show all
Proceedings (peer-reviewed)
ISBN
978-3-030-33245-7
Title of proceedings
On the Move to Meaningful Internet Systems: OTM 2019 Conferences
Place
Rhodes, Greece
DOI
10.1007/978-3-030-33246-4_38
Open Access
URL
http://doi.org/10.1007/978-3-030-33246-4
Type of Open Access
Publisher (Gold Open Access)
Abstract
Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no “one model fits all”), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.
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