Revealing the Conceptual Schemas of RDF Datasets

Subhi Issa 1 Pierre-Henri Paris 1 Fayçal Hamdi 1 Samira Si-Said Cherfi 1
1 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
CEDRIC - Centre d'études et de recherche en informatique et communications
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.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-02424277
Contributor : Samira Si-Said Cherfi <>
Submitted on : Thursday, December 26, 2019 - 10:36:05 PM
Last modification on : Friday, January 24, 2020 - 4:06:06 PM

Identifiers

Collections

Citation

Subhi Issa, Pierre-Henri Paris, Fayçal Hamdi, Samira Si-Said Cherfi. Revealing the Conceptual Schemas of RDF Datasets. 31st International Conference, CAiSE 2019, Jun 2019, Rome, Italy. pp.312-327, ⟨10.1007/978-3-030-21290-2_20⟩. ⟨hal-02424277⟩

Share

Metrics

Record views

44