表示学习知识图谱的实体对齐算法

与现有的根据知识图谱的结构信息或实体属性特征进行相似度匹配的实体对齐的方法不同，提出了一种基于表示学习的知识图谱实体对齐方法.首先，在低维向量空间下，通过机器学习方法学得实体和关系的语义表示，这种表示形式蕴含了知识图谱的内在结构信息及实体属性特征;其次，将人工标注的实体对作为先验知识，学习知识图谱间实体对的映射关系.经实验验证表明:与基于特征匹配的方法SiGMa相比，本文方法能够有效提高知识图谱实体对齐的精确率，同时保持较高的F1值.

Entity Alignment Algorithm for Knowledge Graph of Representation Learning

A novel supervised method for knowledge graph entity alignment based on representation learning was proposed， which is different from the existing methods due to the similarity of structural information or attributive characters. First， the method automatically learns the semantic representations for the entities and relations of a knowledge graph in a low-dimensional vector space was proposed， and these embeddings contain the intrinsically structural information of a knowledge graph and the attributive features of entities. Afterwards， taking the manually aligned entity pairs as prior knowledge， the cross-KG mapping relationship between entities could be learned， which will be used for predicting entity alignment. Experiments conducted on real datasets demonstrated that our method can effectively improve the precision of knowledge graph entity alignment while keeping a high F1 score， when compared with the feature matching based method SiGMa.