Abstract
The number of publicly accessible knowledge graphs is increasing and so are their applications. Knowledge graphs may contain private data and need to be protected against unauthorized access. There are different approaches for access control to knowledge graphs, e.g., user-based or policy-based. User-based access control can be hard to maintain in systems with hundreds or even thousands of users. In contrast, policy-based approaches use rules to decide whether the access should be granted or denied. ODRL is designed for licensing but also used for policy-based access control. Hence, the evaluation of access policies is not defined and no external data can be considered during the decision-making process. Policies can be seen as integrity constraints and, hence, it is natural to specify them in SHACL; the semantics of SHACL validation are well-defined. SHACL-ACL demonstrates how SHACL can be utilized in a policy-based access control approach. Furthermore, utilizing RML mappings, SHACL-ACL is capable of considering data from various heterogeneous sources for the policy evaluation, e.g., JSON data from Web APIs. The demo is available as an interactive Jupyter notebook.
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Notes
- 1.
The code is available at https://github.com/SDM-TIB/SHACL-ACL.
- 2.
The live demo is available at https://mybinder.org/v2/gh/SDM-TIB/SHACL-ACL/HEAD?labpath=SHACL-ACL.ipynb.
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Acknowledgements
This work has been partially supported by the EU H2020 RIA funded project CLARIFY (grant agreement No 875160) and the Federal Ministry for Economic Affairs and Energy of Germany (BMWK) in the project CoyPu (project number 01MK21007[A-L]).
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Rohde, P.D., Iglesias, E., Vidal, ME. (2023). SHACL-ACL: Access Control with SHACL. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_4
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