A Graph-Native Approach to Normalization

TL;DR

This paper introduces a graph-native normalization approach for knowledge graphs, considering dependencies within nodes, edges, and their combinations to improve data quality.

Contribution

It extends existing normalization methods by incorporating edge dependencies and proposes algorithms for graph normalization based on new normal forms.

Findings

Proposed graph-native normal forms and functional dependencies.

Algorithms for transforming graphs into normalized forms.

Evaluation on synthetic and real graph datasets showing effectiveness.

Abstract

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.