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.

中文翻译：

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Graph2Seq：用于知识图完成的融合嵌入学习

知识图谱（KG）通常包含关于现实世界的数十亿个事实，其中一个事实以（头实体、关系、尾实体）的形式表示为三元组。KG 是一个复杂的网络，由众多节点（实体）和边（关系）组成。鉴于大多数 KG 都是嘈杂的并且远非完整，KG 分析和完成方法变得越来越重要。知识图嵌入（KGE）旨在将实体和关系嵌入到低维连续向量空间中，这在知识图完成任务中被证明是一种非常有效的方法。KGE 模型设计了各种评分函数来评估 KG 中的每个事实，为真实事实分配高分，为无效事实分配低分。在现实世界的 KG 中，某些节点可能与其他节点有数百条链接。一个实体周围有丰富的信息，一个实体的周围信息（即子图结构信息）可以对预测新的事实做出重大贡献。然而，许多以前的工作，包括 Trans(E, H, R, and D) 等平移方法，DistMult、ComplEx 等因式分解方法，以及 NTN、ConvE 等其他深度学习方法，都专注于对孤立中的每个事实进行评分。并且分离方式，缺乏专门设计的机制来学习KG中实体的子图结构信息。为了克服这一挑战，我们利用专为图结构数据设计的图神经网络中使用的信息融合机制（Graph2Seq）来学习 KG 中实体的融合嵌入。并提出了一种新的融合嵌入学习KGE模型（简称G2SKGE），旨在学习KG中实体的子图结构信息。通过对四个基准数据集的实证实验，我们提出的模型取得了有希望的结果并优于最先进的模型。