SHACTOR: Improving the Quality of Large-Scale Knowledge Graphs with Validating Shapes

Kashif Rabbani, Matteo Lissandrini, and Katja Hose

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The final authenticated version is available online at 10.1145/3555041.3589723.

This work will be presented Tue. June 20th, 2023

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.

Cite:

and
“SHACTOR: Improving the Quality of Large-Scale Knowledge Graphs with Validating Shapes.” Companion of the 2023 International Conference on Management of Data (SIGMOD’23) . 

@inbook{RabbaniSHACTOR23,
author = {Rabbani, Kashif and Lissandrini, Matteo and Hose, Katja},
title = {SHACTOR: Improving the Quality of Large-Scale Knowledge Graphs with Validating Shapes},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = "10.1145/3555041.3589723"
booktitle = {Companion of the 2023 International Conference on Management of Data},
numpages = {4}
}