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

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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.

Original languageEnglish
Title of host publicationWWW 2022 - Companion Proceedings of the Web Conference 2022
Number of pages4
PublisherAssociation for Computing Machinery
Publication date25 Apr 2022
ISBN (Electronic)9781450391306
Publication statusPublished - 25 Apr 2022
Event31st ACM Web Conference, WWW 2022 - Virtual, Online, France
Duration: 25 Apr 2022 → …


Conference31st ACM Web Conference, WWW 2022
CityVirtual, Online
Period25/04/2022 → …
SeriesWWW 2022 - Companion Proceedings of the Web Conference 2022

Bibliographical note

Publisher Copyright:
© 2022 ACM.


  • Knowledge Graphs
  • Shapes Extraction
  • ShEx


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