调查实体嵌入模型和关系关系,以完成知识图表 (A survey of embedding models of entities and relationships for knowledge graph completion)

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.

翻译：关于实体及其关系的真实世界事实知识图(KGs)是各种自然语言处理任务的有用资源,然而,由于知识图通常是不完整的,因此有必要进行知识图的完成或联系预测,即预测知识图中不存在的关系是否可能是真实的,本文件是对实体嵌入模型和知识图完成关系的全面调查,总结标准基准数据集的最新实验结果,并指出潜在的未来研究方向。