Is SHACL Suitable for Data Quality Assessment?

2025-07-29

AI Summary

Knowledge graphs (KGs) lack rigid schemas, leading to pervasive data quality issues. To address this, this study systematically evaluates SHACL’s applicability for KG data quality assessment against Zaveri et al.’s multidimensional data quality framework. Method: We comprehensively model 69 quality metrics using SHACL shape definitions augmented with SPARQL queries, enabling automated instantiation and reuse. A prototype system for scalable, rule-based data quality validation is implemented. Contribution/Results: (1) Empirical evaluation confirms SHACL’s expressive adequacy for major quality dimensions—including accuracy, completeness, consistency, and timeliness; (2) we publicly release the full set of SHACL constraints and an integrated toolchain, supporting automated, standardized, and continuous KG quality measurement and monitoring. Results demonstrate that SHACL serves as a robust, standardized, and extensible foundation for operationalizing data quality assessment in knowledge graphs.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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

Problem

• Assess SHACL's suitability for knowledge graph quality evaluation
• Cover comprehensive data quality dimensions using SHACL shapes
• Automate quality metric implementation via SHACL validation results

Innovation

• Uses SHACL for data quality assessment
• Implements 69 data quality metrics
• Automates SHACL shape instantiation and validation