Carolina Cortés | Searcharxiv

arXiv2026

WiDiff: Extracting Changes from Wikidata's Edit History

Knowledge graphs have become a key resource for integrating heterogeneous data and powering downstream tasks such as question answering, entity linking, and semantic search. They are built and maintained incrementally, either (i) fully automated, e.g., YAGO, (ii) semiautomatically with community oversight, e.g., DBpedia, or (iii) manually through collaborative editing, e.g., Wikidata. Understanding the evolution of knowledge graphs is essential as changes may reflect real-world updates, error corrections, or noise introduced by vandalism, all of which affect the reliability of downstream applications. Among openly available knowledge graphs, Wikidata is the most challenging case to study evolution, with over 120 million entities edited by humans and bots and an edit history spanning more than a decade. Although Wikidata exposes change data in various formats (e.g., periodic dumps and real-time event streams), none support analytical queries over the complete edit history. Therefore, we present WiDiff, a tool that extracts changes from Wikidata's complete edit history and provides a unified interface for large-scale analytical queries over it.

cs.DB↗

arXiv2025

Is SHACL Suitable for Data Quality Assessment?

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.

cs.DB↗