人工智能时代知识图谱表示学习方法体系

 总结了不含辅助信息的知识图谱表示学习方法，主要是基于距离和基于语义匹配2类主流方法；研究了包含文本辅助信息和类别辅助信息的知识图谱表示学习方法；通过对比各类表示学习方法的优缺点，发现引入辅助信息能有效表达知识图谱中新实体，但时空开支大幅上升，因而在现阶段，不含辅助信息的方法更易应用于实际场景中。分析了知识图谱嵌入如何应用于三元组分类、链路预测、推荐系统等下游任务，整理归纳了应用于不同任务的数据集和开源库的集合，并展望了大规模、动态知识图谱等具有广泛应用前景的研究方向。

Knowledge graph representation learning method system in the era of artificial intelligence

In recent years, the knowledge graph representation learning has been used to represent the components of the knowledge graphs in a low-dimensional vector embedding, as a mainstream way to combine the artificial intelligence with the knowledge graphs. This paper reviews the mainstream knowledge graph representation learning methods without auxiliary information, mainly, the distance-based and the semantic matching-based methods, and the knowledge graph representation learning methods containing textual auxiliary information and category auxiliary information, along with the advantages and the disadvantages of various representation learning methods. It is found that the introduction of auxiliary information can effectively represent new entities and relationships in the knowledge graph, but the time and space costs are significantly increased, and thus the methods without auxiliary information are more easily applied in practical scenarios at this stage. Finally, we show how the knowledge graph embedding can be applied to downstream tasks such as the triad classification, the link prediction and the recommender systems. A collection of datasets and open source libraries for different tasks is compiled and, and a comprehensive outlook on promising research directions such as large-scale, dynamic knowledge graphs is given.