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2019 | OriginalPaper | Buchkapitel

Revealing the Conceptual Schemas of RDF Datasets

verfasst von : Subhi Issa, Pierre-Henri Paris, Fayçal Hamdi, Samira Si-Said Cherfi

Erschienen in: Advanced Information Systems Engineering

Verlag: Springer International Publishing

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Abstract

RDF-based datasets, thanks to their semantic richness, variety and fine granularity, are increasingly used by both researchers and business communities. However, these datasets suffer a lack of completeness as the content evolves continuously and data contributors are loosely constrained by the vocabularies and schemes related to the data sources. Conceptual schemas have long been recognized as a key mechanism for understanding and dealing with complex real-world systems. In the context of the Web of Data and user-generated content, the conceptual schema is implicit. In fact, each data contributor has an implicit personal model that is not known by the other contributors. Consequently, revealing a meaningful conceptual schema is a challenging task that should take into account the data and the intended usage. In this paper, we propose a completeness-based approach for revealing conceptual schemas of RDF data. We combine quality evaluation and data mining approaches to find a conceptual schema for a dataset, this model meets user expectations regarding data completeness constraints. To achieve that, we propose LOD-CM; a web-based completeness demonstrator for linked datasets.

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Vorheriges Kapitel A Fourth Normal Form for Uncertain Data

Nächstes Kapitel Modeling and In-Database Management of Relational, Data-Aware Processes

Performed on: http://dbpedia.org/sparql.

http://cedric.cnam.fr/lod-cm.

http://plantuml.com/.

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Titel: Revealing the Conceptual Schemas of RDF Datasets
verfasst von: Subhi Issa
Pierre-Henri Paris
Fayçal Hamdi
Samira Si-Said Cherfi
Verlag: Springer International Publishing
Buch: Advanced Information Systems Engineering
Print ISBN: 978-3-030-21289-6

Electronic ISBN: 978-3-030-21290-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-21290-2_20

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