A Query-Driven Approach for SHACL Type Inference
Haller D (2023)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Series: CEUR Workshop Proceedings
Book Volume: 3452
Pages Range: 41--44
Conference Proceedings Title: Proceedings of the VLDB 2023 PhD Workshop co-located with the 49th International Conference on Very Large Data Bases (VLDB 2023)
Event location: Vancouver
URI: https://ceur-ws.org/Vol-3452/paper11.pdf
Open Access Link: https://ceur-ws.org/Vol-3452/
Abstract
The Semantic Web enables everyone to share knowledge that can be reused in different applications. While the use of a formal ontology describing the semantics of the shared data is encouraged, it cannot be enforced and is often done incorrectly, incompletely, or not at all. However, the semantics are present in the minds of those working with the data, as manifested in all the SPARQL queries they have written. Therefore, analyzing these query logs helps us to learn these semantics and allows us to construct a graph of SHACL shapes describing the types and their constraints of a data source, which can serve as the foundation for a human-in-the-loop approach to further extend and correct the generated schema.
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APA:
Haller, D. (2023). A Query-Driven Approach for SHACL Type Inference. In Vasilis Efthymiou, Xiao Hu (Eds.), Proceedings of the VLDB 2023 PhD Workshop co-located with the 49th International Conference on Very Large Data Bases (VLDB 2023) (pp. 41--44). Vancouver, CA.
MLA:
Haller, David. "A Query-Driven Approach for SHACL Type Inference." Proceedings of the VLDB 2023 PhD Workshop, Vancouver Ed. Vasilis Efthymiou, Xiao Hu, 2023. 41--44.
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