论文标题
上下文增强的实体和关系嵌入知识图完成
Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion
论文作者
论文摘要
大多数知识图完成的研究都会学习实体和关系的表示，以预测不完整的知识图中缺少链接。但是，这些方法无法充分利用实体和关系的上下文信息。在这里，我们从它们构成的三胞胎中提取实体和关系的上下文。我们提出了一个名为Aggre的模型，该模型分别在多求中的实体上下文和关系上下文上进行有效的聚合，并学习上下文增强的实体和关系嵌入以进行知识图完成。实验结果表明，Aggre与现有模型具有竞争力。
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.
