用图差分依赖提升图数据缺失值填补的可靠性与可解释性
GIG: Graph Data Imputation With Graph Differential Dependencies
- 基于图差分依赖（GDDs）学习语义规则，指导填补过程
- 在7个真实数据集上优于现有最先进方法，提升填补准确率
- 适合需要可解释性的图数据修复场景，如知识图谱
数据填补旨在恢复数据库实例中的缺失值，确保与数据集整体语义一致。尽管已有基于统计方法和特定规则的启发式算法，但其泛化能力差且缺乏数据上下文支持，导致可解释性不足。现有方法多聚焦于关系型数据，难以应用于图数据等更广泛场景。本文提出GIG方法，利用图差分依赖（GDDs）进行图数据填补。GIG从知识图谱中学习GDDs，并以此训练Transformer模型，预测图中缺失数据的值。通过引入语义知识，GIG提升了填补的可靠性与可解释性。在七个真实世界数据集上的实验表明，GIG显著优于现有最先进的方法。
原文摘要 · Abstract (English)
Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been proposed. These do not generalise well and often lack data context. Consequently, they also lack explainability. The existing techniques also mostly focus on the relational data context making them unsuitable for wider application contexts such as in graph data. In this paper, we propose a graph data imputation approach called GIG which relies on graph differential dependencies (GDDs). GIG, learns the GDDs from a given knowledge graph, and uses these rules to train a transformer model which then predicts the value of missing data within the graph. By leveraging GDDs, GIG incoporates semantic knowledge into the data imputation process making it more reliable and explainable. Experimental results on seven real-world datasets highlight GIG's effectiveness compared to existing state-of-the-art approaches.
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