论文标题
建模基于卷积的知识图嵌入的多关系
Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding
论文作者
论文摘要
知识图的表示旨在将实体和关系嵌入到低维矢量中。大多数现有作品仅考虑实体对之间的直接关系或路径。据认为，这种方法断开了实体对之间多关系的语义连接，我们提出了卷积和多关系表示模型Convermr。提出的ConvMR模型在两个方面解决了多关系问题：（1）将实体对之间的多关系编码到维持语义连接的统一向量中。 （2）由于加入多关系时，并非所有关系都是必要的，因此我们建议基于注意力的关系编码器以基于语义层次结构自动为不同的关系分配权重。在两个流行的数据集FB15K-237和WN18RR上进行的实验结果在平均等级方面取得了一致的改进。我们还发现，CORVMR有效地处理较频繁的实体。
Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR. The proposed ConvMR model addresses the multi-relation issue in two aspects: (1) Encoding the multi-relations between an entity pair into a unified vector that maintains the semantic connection. (2) Since not all relations are necessary while joining multi-relations, we propose an attention-based relation encoder to automatically assign weights to different relations based on semantic hierarchy. Experimental results on two popular datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean rank. We also found that ConvMR is efficient to deal with less frequent entities.
