Is SHACL Suitable for Data Quality Assessment? (2507.22305v1)

Abstract: Knowledge graphs have been widely adopted in both enterprises, such as the Google Knowledge Graph, and open platforms like Wikidata to represent domain knowledge and support analysis with artificial intelligence. They model real-world information as nodes and edges. To embrace flexibility, knowledge graphs often lack enforced schemas (i.e., ontologies), leading to potential data quality issues, such as semantically overlapping nodes. Therefore, ensuring their quality is essential, as issues in the data can affect applications relying on them. To assess the quality of knowledge graphs, existing works either propose high-level frameworks comprising various data quality dimensions without concrete implementations, define tools that measure data quality with ad-hoc SPARQL (SPARQL Protocol and RDF Query Language) queries, or promote the usage of constraint languages, such as the Shapes Constraint Language (SHACL), to assess and improve the quality of the graph. Although the latter approaches claim to address data quality assessment, none of them comprehensively tries to cover all data quality dimensions. In this paper, we explore this gap by investigating the extent to which SHACL can be used to assess data quality in knowledge graphs. Specifically, we defined SHACL shapes for 69 data quality metrics proposed by Zaveri et al. [1] and implemented a prototype that automatically instantiates these shapes and computes the corresponding data quality measures from their validation results. All resources are provided for repeatability at https://github.com/caroocortes/SHACL-DQA-prototype/tree/main