Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | VLDB 2021 PhD Workshop |
| Untertitel | Proceedings of the VLDB 2021 PhD Workshop co-located with the 47th International Conference on Very Large Databases (VLDB 2021) |
| Seitenumfang | 4 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 2021 International Conference on Very Large Databases PhD Workshop, VLDB-PhD 2021 - Copenhagen, Dänemark Dauer: 16 Aug. 2021 → … |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2971 |
| ISSN (Print) | 1613-0073 |
Abstract
The importance of knowledge graphs is increasing. Due to their application in more and more real-world use-cases the data quality issue has to be addressed. The Shapes Constraint Language (SHACL) is the W3C recommendation language for defining integrity constraints over knowledge graphs expressed in the Resource Description Framework (RDF). Annotating SPARQL query results with metadata from the SHACL validation provides a better understanding of the knowledge graph and its data quality. We propose a query engine that is able to efficiently evaluate which instances in the knowledge graph fulfill the requirements from the SHACL shape schema and annotate the SPARQL query result with this metadata. Hence, adding the dimension of explainability to SPARQL query processing. Our preliminary analysis shows that the proposed optimizations performed for SHACL validation during SPARQL query processing increase the performance compared to a naive approach. However, in some queries the naive approach outperforms the optimizations. This shows that more work needs to be done in this topic to fully comprehend all impacting factors and to identify the amount of overhead added to the query execution.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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VLDB 2021 PhD Workshop: Proceedings of the VLDB 2021 PhD Workshop co-located with the 47th International Conference on Very Large Databases (VLDB 2021). 2021. (CEUR Workshop Proceedings; Band 2971).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - SHACL constraint validation during SPARQL query processing
AU - Rohde, Philipp D.
AU - Vidal, Maria Esther
N1 - Funding Information: This work has been partially supported by the EU H2020 RIA funded projects QualiChain (No 822404) and CLARIFY (No 875160), and the ERAMed project P4-LUCAT (No 53000015).
PY - 2021
Y1 - 2021
N2 - The importance of knowledge graphs is increasing. Due to their application in more and more real-world use-cases the data quality issue has to be addressed. The Shapes Constraint Language (SHACL) is the W3C recommendation language for defining integrity constraints over knowledge graphs expressed in the Resource Description Framework (RDF). Annotating SPARQL query results with metadata from the SHACL validation provides a better understanding of the knowledge graph and its data quality. We propose a query engine that is able to efficiently evaluate which instances in the knowledge graph fulfill the requirements from the SHACL shape schema and annotate the SPARQL query result with this metadata. Hence, adding the dimension of explainability to SPARQL query processing. Our preliminary analysis shows that the proposed optimizations performed for SHACL validation during SPARQL query processing increase the performance compared to a naive approach. However, in some queries the naive approach outperforms the optimizations. This shows that more work needs to be done in this topic to fully comprehend all impacting factors and to identify the amount of overhead added to the query execution.
AB - The importance of knowledge graphs is increasing. Due to their application in more and more real-world use-cases the data quality issue has to be addressed. The Shapes Constraint Language (SHACL) is the W3C recommendation language for defining integrity constraints over knowledge graphs expressed in the Resource Description Framework (RDF). Annotating SPARQL query results with metadata from the SHACL validation provides a better understanding of the knowledge graph and its data quality. We propose a query engine that is able to efficiently evaluate which instances in the knowledge graph fulfill the requirements from the SHACL shape schema and annotate the SPARQL query result with this metadata. Hence, adding the dimension of explainability to SPARQL query processing. Our preliminary analysis shows that the proposed optimizations performed for SHACL validation during SPARQL query processing increase the performance compared to a naive approach. However, in some queries the naive approach outperforms the optimizations. This shows that more work needs to be done in this topic to fully comprehend all impacting factors and to identify the amount of overhead added to the query execution.
UR - http://www.scopus.com/inward/record.url?scp=85117054341&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85117054341
T3 - CEUR Workshop Proceedings
BT - VLDB 2021 PhD Workshop
T2 - 2021 International Conference on Very Large Databases PhD Workshop, VLDB-PhD 2021
Y2 - 16 August 2021
ER -