A Graph-Native Approach to Normalization
This work provides a foundational framework for improving knowledge graph quality, addressing a previously underexplored problem for practitioners managing graph data.
The paper introduces graph-native normalization for labeled property graphs, addressing redundancies and inconsistencies by considering dependencies within nodes, edges, and their combination. It defines normal forms and algorithms, demonstrating effectiveness on synthetic and real-world datasets.
In recent years, knowledge graphs (KGs) - in particular in the form of labeled property graphs (LPGs) - have become essential components in a broad range of applications. Although the absence of strict schemas for KGs facilitates structural issues that lead to redundancies and subsequently to inconsistencies and anomalies, the problem of KG quality has so far received only little attention. Inspired by normalization using functional dependencies for relational data, a first approach exploiting dependencies within nodes has been proposed. However, real-world KGs also expose functional dependencies involving edges. In this paper, we therefore propose graph-native normalization, which considers dependencies within nodes, edges, and their combination. We define a range of graph-native normal forms and graph object functional dependencies and propose algorithms for transforming graphs accordingly. We evaluate our contributions using a broad range of synthetic and native graph datasets.