Rabbani, K., Lissandrini, M., & Hose, K. (2023). SHACTOR: Improving the Quality of Large-Scale Knowledge Graphs with Validating Shapes. In Companion of the 2023 International Conference on Management of Data (pp. 151–154). https://doi.org/10.1145/3555041.3589723
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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Published in:
Companion of the 2023 International Conference on Management of Data
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ISBN:
9781450395076
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Date (published):
5-Jun-2023
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Event name:
SIGMOD/PODS '23: International Conference on Management of Data
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Event date:
18-Jun-2023 - 23-Jun-2023
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Event place:
Seattle, WA, United States of America (the)
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Number of Pages:
4
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Peer reviewed:
Yes
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Keywords:
knowledge graphs; quality assessment; SHACL; shapes extraction; Analyze; SPARQL; Data
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Abstract:
We demonstrate SHACTOR, a system for extracting and analyzing validating shapes from very large Knowledge Graphs (KGs). Shapes represent a specific form of data patterns, akin to schemas for entities. Standard shape extraction approaches are likely to produce thousands of shapes, and some of those represent spurious constraints extracted due to the presence of erroneous data in the KG. Given a KG having tens of millions of triples and thousands of classes, SHACTOR parses the KG using our efficient and scalable shapes extraction algorithm and outputs SHACL shapes constraints. The extracted shapes are further annotated with statistical information regarding their support in the graph, which allows to identify both erroneous and missing triples in the KG. Hence, SHACTOR can be used to extract, analyze, and clean shape constraints from very large KGs. Furthermore, it enables the user to also find and correct errors by automatically generating SPARQL queries over the graph to retrieve nodes and facts that are the source of the spurious shapes and to intervene by amending the data.
