论文标题
知识图完成的关系消息传递
Relational Message Passing for Knowledge Graph Completion
论文作者
论文摘要
知识图完成旨在预测知识图中实体之间的丢失关系。在这项工作中，我们提出了一种关系图形完成的关系消息传递方法。与现有的基于嵌入的方法不同，关系消息传递仅考虑Edge功能（即关系类型），而没有实体ID在知识图中，并在迭代中传递关系信息以汇总社区信息。具体而言，在关系消息传递框架下为给定实体对建模了两种邻域拓扑：（1）关系上下文，它们捕获了与给定实体对相邻的边缘的关系类型； （2）关系路径，它表征了知识图中给定两个实体之间的相对位置。将两个消息传递模块组合在一起以进行关系预测。知识图基准以及我们新提出的数据集的实验结果表明，我们的方法路径的表现优于最先进的知识图完成方法。路径孔还适用于在训练阶段未见实体的归纳设置，并且能够为预测结果提供可解释的解释。代码和所有数据集可在https://github.com/hwwang55/pathcon上找到。
Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph completion. Different from existing embedding-based methods, relational message passing only considers edge features (i.e., relation types) without entity IDs in the knowledge graph, and passes relational messages among edges iteratively to aggregate neighborhood information. Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph. The two message passing modules are combined together for relation prediction. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that, our method PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. PathCon is also shown applicable to inductive settings where entities are not seen in training stage, and it is able to provide interpretable explanations for the predicted results. The code and all datasets are available at https://github.com/hwwang55/PathCon.
