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.
翻译:知识图的完成旨在在知识图中预测实体间缺失的关系。 在这项工作中, 我们提出一种关联信息传递方法, 用于知识图的完成。 不同于现有的嵌入式方法, 关联信息传递仅考虑到边缘特征( 关系类型), 在知识图中没有实体标识, 在边缘之间传递关联信息, 并迭接到相邻信息。 具体地说, 在关联信息传递框架下, 为特定实体配对建模了两种相邻区域地形学模型:(1) 关联环境, 它捕捉了相邻实体相邻边缘的关联类型; (2) 关联路径, 描述了在知识图中给定的两个实体之间的相对位置。 两个信息传递模块是结合在一起进行关联预测的。 知识图基准的实验结果以及我们新提议的数据集显示, 我们的方法“ 路径康”(PathConCon) 以一个大边距构建了状态知识图的完成方法。 路径康康(PathCon) 也显示适用于不在培训阶段看到实体的感化环境, 并且它能够为预测结果提供可解释的解释性的解释性解释。 代码和所有数据系统都在 http://Congs/ gast/ 。