User-Centric Property Graph Repairs

Property graphs serve as unifying abstractions for encoding, inspecting, and updating interconnected data with greater expressive power. They are increasingly popular across various application domains involving real users. However, graph data often contains inconsistencies that need proper transformations to address underlying constraint violations and often require specific domain knowledge. In this paper, we propose an interactive and user-centric approach to repair property graphs under denial constraints. Our approach includes a novel theoretical framework comprising a query-based inconsistency detection mechanism, a dependency graph for tracking violations, and an assignment algorithm facilitating multi-user property graph repairs by leveraging independent sets. We evaluate our approach through several experiments on real-world and synthetic datasets, considering different levels of user expertise and comparing against various baselines. Even with multiple non-oracle users, our approach outperforms existing interactive and non-interactive baselines by 30% on average in terms of repair quality. Additionally, we conduct a user study to assess real user performance in property graph repairs.