Is SHACL Suitable for Data Quality Assessment?

TL;DR

This paper investigates the applicability of SHACL for comprehensive data quality assessment in knowledge graphs by defining shapes for multiple metrics and implementing an automated prototype.

Contribution

It introduces a systematic approach to using SHACL for measuring various data quality dimensions in knowledge graphs, filling a gap in existing methods.

Findings

SHACL shapes were defined for 69 data quality metrics.

A prototype was implemented to automatically assess data quality.

Resources are provided for reproducibility.

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 artificial intelligence applications. 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. Yet 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 propose either high-level frameworks comprising various data quality dimensions without concrete implementations, define tools that measure data quality with ad-hoc SPARQL 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.