Graph2Seq: Fusion Embedding Learning for Knowledge Graph Completion

Knowledge Graph (KG) usually contains billions of facts about the real world, where a fact is represented as a triplet in the form of (head entity, relation, tail entity). KG is a complex network and consists of numerous nodes (entities) and edges (relations). Given that most KGs are noisy and far from being complete, KG analysis and completion methods are becoming more and more important. Knowledge graph embedding (KGE) aims to embed entities and relations in a low dimensional and continuous vector space, which is proven to be a quite efficient and effective method in knowledge graph completion tasks. KGE models devise various kinds of score functions to evaluate each fact in KG, which assign high points for true facts and low points for invalid ones. In a KG of the real world, some nodes may have hundreds of links with other nodes. There is a wealth of information around an entity, and the surrounding information (i.e., the sub-graph structure information) of one entity can make a significant contribution to predicting new facts. However, many previous works including, translational approaches such as Trans(E, H, R, and D), factorization approaches such as DistMult, ComplEx, and other deep learning approaches such as NTN, ConvE, concentrate on rating each fact in an isolated and separated way and lack a specially designed mechanism to learn the sub-graph structure information of the entity in KG. To conquer this challenge, we leverage the information fusion mechanism (Graph2Seq) used in graph neural network which is specially designed for graph-structured data, to learn fusion embeddings for entities in KG. And a novel fusion embedding learning KGE model (referred as G2SKGE) which aims to learn the sub-graph structure information of the entity in KG is proposed. With empirical experiments on four benchmark datasets, our proposed model achieves promising results and outperforms the state-of-the-art models.