Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assume that all entities can be new, they do not allow new relations to appear at inference time. This restriction prohibits the existing methods from appropriately handling real-world knowledge graphs where new entities accompany new relations. In this paper, we propose an INductive knowledge GRAph eMbedding method, InGram, that can generate embeddings of new relations as well as new entities at inference time. Given a knowledge graph, we define a relation graph as a weighted graph consisting of relations and the affinity weights between them. Based on the relation graph and the original knowledge graph, InGram learns how to aggregate neighboring embeddings to generate relation and entity embeddings using an attention mechanism. Experimental results show that InGram outperforms 14 different state-of-the-art methods on varied inductive learning scenarios.

翻译：归纳式知识图谱补全被定义为预测训练中未观察到的新实体之间缺失三元组的任务。尽管大多数归纳式知识图谱补全方法假设所有实体都可能是新的，但它们不允许在推理阶段出现新的关系。这一限制使得现有方法无法妥善处理真实世界的知识图谱，因为新实体往往伴随新关系出现。本文提出了一种归纳式知识图谱嵌入方法——InGram，该方法能够在推理阶段同时生成新关系和新实体的嵌入。给定一个知识图谱，我们将关系图定义为由关系及其间亲和权重构成的加权图。基于关系图和原始知识图谱，InGram学习如何通过注意力机制聚合邻域嵌入，从而生成关系和实体的嵌入。实验结果表明，InGram在多种不同的归纳学习场景下优于14种当前最先进的方法。