An Error Taxonomy for Property Graph Data
Graph data are increasingly used in applications where relationships and topology matter. Yet, compared to tabular data, the notions of data quality and error types for graphs are less established. Tabular error concepts often fail to capture structural properties and context inherent in graph data, hindering systematic quality assessment in graph-based systems. We focus on property graphs (PGs) in graph databases. PGs are often managed without a prescriptive schema, enabling data insertion without explicit validation, while still exhibiting an implicit schema of recurring structural patterns. Irregularities in these patterns may indicate errors, but their interpretation depends on the surrounding graph context. To address these challenges, we provide a systematic view of errors in graph data by proposing a comprehensive graph-specific taxonomy of error types. To position these errors with respect to established work on tabular data quality, we derive a mapping between graph errors and corresponding error notions in tabular data. This mapping highlights which concepts transfer across data models and where structural properties of graphs introduce fundamentally different challenges requiring extensions. Overall, the taxonomy and mapping provide a shared vocabulary for describing, characterizing, comparing, and studying graph data errors, and they lay the groundwork for future work on error detection and repair in graph data.