Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multi-hops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.
翻译：大多数用于完成知识图的研究工作都学习了实体和关系的表述,以预测不完整知识图中缺失的环节,然而,这些方法未能充分利用实体和关系的背景信息。在这里,我们从它们组成的三重关系中提取了实体和关系的背景。我们提出了一个名为Aggrle的模型,分别对实体背景和多窗口关系背景进行有效的汇总,并学习了环境强化实体和关联嵌入知识图的完成。实验结果表明,Aggrle对现有模式具有竞争力。
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