# Knowledge Embedding Based Graph Convolutional Network Source: [Knowledge Embedding Based Graph Convolutional Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:08:38.583Z Authors: Donghan Yu, Yiming Yang, Ruohong Zhang, Yuexin Wu Published: 2020-06-12T17:12:51.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Entity Alignment on DICEWS-200 Less Seed Setting (test)](): Entity Alignment; DICEWS-200 Less Seed Setting (test); H@1: 37.3. - [Entity Alignment on WY50K-1K Less Seed Setting (test)](): Entity Alignment; WY50K-1K Less Seed Setting (test); H@1: 60. - [Entity Alignment on DICEWS-1K Normal Setting (10% seeds) (test)](): Entity Alignment; DICEWS-1K Normal Setting (10% seeds) (test); H@1: 54.9. - [Entity Alignment on WY50K-5K Normal Setting (10% seeds) (test)](): Entity Alignment; WY50K-5K Normal Setting (10% seeds) (test); H@1: 78. ## Original sources and implementation [Original paper]() [PDF]()