Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding

TL;DR

This paper introduces a novel, efficient, and general decoding method for entity alignment in knowledge graphs, leveraging gradient flow and multi-view matrices to improve accuracy and scalability across datasets.

Contribution

It proposes a generalized decoding approach called Triple Feature Propagation (TFP) that relies solely on entity embeddings and enhances existing EA methods with minimal additional computation.

Findings

Significantly improves entity alignment accuracy across datasets.

Achieves state-of-the-art efficiency with less than 6 seconds of extra computation.

Demonstrates robustness and adaptability of the method in diverse scenarios.

Abstract

Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation learning task, concentrating on enhancing graph encoders. However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and additional explicit relation embeddings. This specificity limits its applicability, particularly in GNN-based models. To address this gap, we introduce a novel, generalized, and efficient decoding approach for EA, relying solely on entity embeddings. Our method optimizes the decoding process by minimizing Dirichlet energy, leading to the gradient flow within the graph, to maximize graph homophily. The discretization of the gradient flow produces a fast and scalable approach, termed Triple Feature Propagation (TFP). TFP innovatively generalizes adjacency matrices to multi-views matrices:entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple. The gradient flow through generalized matrices enables TFP to harness the multi-view structural information of KGs. Rigorous experimentation on diverse public datasets demonstrates that our approach significantly enhances various EA methods. Notably, the approach achieves these advancements with less than 6 seconds of additional computational time, establishing a new benchmark in efficiency and adaptability for future EA methods.