SHACL and ShEx in the Wild: A Community Survey on Validating Shapes Generation and Adoption

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Abstrakt

Knowledge Graphs (KGs) are widely used to represent heterogeneous domain knowledge on the Web and within organizations. Various methods exist to manage KGs and ensure the quality of their data. Among these, the Shapes Constraint Language (SHACL) and the Shapes Expression Language (ShEx) are the two state-of-the-art languages to define validating shapes for KGs. Since the usage of these constraint languages has recently increased, new needs arose. One such need is to enable the efficient generation of these shapes. Yet, since these languages are relatively new, we witness a lack of understanding of how they are effectively employed for existing KGs. Therefore, in this work, we answer How validating shapes are being generated and adopted? Our contribution is threefold. First, we conducted a community survey to analyze the needs of users (both from industry and academia) generating validating shapes. Then, we cross-referenced our results with an extensive survey of the existing tools and their features. Finally, we investigated how existing automatic shape extraction approaches work in practice on real, large KGs. Our analysis shows the need for developing semi-automatic methods that can help users generate shapes from large KGs.

OriginalsprogEngelsk
TitelWWW 2022 - Companion Proceedings of the Web Conference 2022
Antal sider4
ForlagAssociation for Computing Machinery
Publikationsdato25 apr. 2022
Sider260-263
ISBN (Elektronisk)9781450391306
DOI
StatusUdgivet - 25 apr. 2022
Begivenhed31st ACM Web Conference, WWW 2022 - Virtual, Online, Frankrig
Varighed: 25 apr. 2022 → …

Konference

Konference31st ACM Web Conference, WWW 2022
Land/OmrådeFrankrig
ByVirtual, Online
Periode25/04/2022 → …
SponsorACM SIGWEB
NavnWWW 2022 - Companion Proceedings of the Web Conference 2022

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