Knowledge Embedding Based Graph Convolutional Network

TL;DR

This paper introduces KE-GCN, a novel framework that integrates knowledge graph embeddings with graph convolutional networks to better leverage complex graph structures, especially in heterogeneous knowledge graphs.

Contribution

It proposes a unified framework that combines GCNs with knowledge embedding techniques, enabling joint propagation and updating of node and edge embeddings for complex graphs.

Findings

KE-GCN outperforms baseline methods in knowledge graph alignment.

KE-GCN achieves superior accuracy in entity classification tasks.

Theoretical analysis unifies existing GCN methods under a new perspective.

Abstract

Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graphs, such as knowledge graphs with heterogeneous types of entities and relations, is a primary open challenge in the field. Most GCN methods are either restricted to graphs with a homogeneous type of edges (e.g., citation links only), or focusing on representation learning for nodes only instead of jointly propagating and updating the embeddings of both nodes and edges for target-driven objectives. This paper addresses these limitations by proposing a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. Our theoretical analysis shows that KE-GCN offers an elegant unification of several well-known GCN methods as specific cases, with a new perspective of graph convolution. Experimental results on benchmark datasets show the advantageous performance of KE-GCN over strong baseline methods in the tasks of knowledge graph alignment and entity classification.