Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. While many different methods have been proposed, there is a lack of a unifying framework that would lead to state-of-the-art results. Here we develop PathCon, a knowledge graph completion method that harnesses four novel insights to outperform existing methods. PathCon predicts relations between a pair of entities by: (1) Considering the Relational Context of each entity by capturing the relation types adjacent to the entity and modeled through a novel edge-based message passing scheme; (2) Considering the Relational Paths capturing all paths between the two entities; And, (3) adaptively integrating the Relational Context and Relational Path through a learnable attention mechanism. Importantly, (4) in contrast to conventional node-based representations, PathCon represents context and path only using the relation types, which makes it applicable in an inductive setting. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. Finally, PathCon is able to provide interpretable explanations by identifying relations that provide the context and paths that are important for a given predicted relation.
翻译：知识图的完成旨在用知识图预测实体之间在知识图中缺失的关系。 虽然提出了许多不同的方法, 但缺乏统一框架, 导致最先进的结果。 我们在这里开发了“ 路透”, 这是一种知识图的完成方法, 利用四种新的洞察力优于现有方法。 路透C 通过以下方式预测一对实体之间的关系:(1) 通过捕捉与实体相邻的关联类型, 并通过一个新的边缘信息传递计划建模;(2) 考虑包含两个实体之间所有路径的“关系路径”;(3) 通过一个可学习的注意机制, 适应性地整合“ 关系背景和关系路径”。 重要的是, “ 路透” 能够提供可解释的解释性解释性解释性解释性解释性解释性, 与传统的基于节点的表达方式相比, “ 路透” 代表背景和路径, 仅使用关系类型, 从而在感应适用于感地环境。 知识图基准的实验结果以及我们新提议的数据集显示, “ 路透” 以一个大边缘的“ ” 。 最后, “ 路透” 能够提供为重要背景和路径的预期关系提供可解释性解释性的解释性解释性解释性解释性解释性解释性解释性关系。