计算机工程与应用 ›› 2023, Vol. 59 ›› Issue (18): 59-73.DOI: 10.3778/j.issn.1002-8331.2212-0119
于梦波,杜建强,罗计根,聂斌,刘勇,邱俊洋
出版日期:
2023-09-15
发布日期:
2023-09-15
YU Mengbo, DU Jianqiang, LUO Jigen, NIE Bin, LIU Yong, QIU Junyang
Online:
2023-09-15
Published:
2023-09-15
摘要: 知识图谱(KG)是一种基于图的数据结构,其知识是以三元组的形式呈现,即(头实体,关系,尾实体)。随着人工智能的发展,知识图谱已在系统推荐、智能问答、知识搜索等领域发挥了重要作用。然而构建的知识图谱具有不完整性,影响了知识图谱的下游任务应用,知识图谱补全能够很好地解决这一问题。近年来,基于知识表示学习的知识图谱补全方法成为研究的热点,其以表示向量的形式在低维连续向量空间中学习实体和关系的嵌入特征,旨在预测未知的事实信息进行知识图谱补全。根据KG类型的不同,将其分为静态知识图谱补全、时序知识图谱补全以及多模态知识图谱补全,对这三类知识图谱补全方法拟解决的关键问题、设计思路、模型评价等方面进行对比总结,展望知识图谱补全未来的发展方向,为相关领域的研究人员提供参考。
于梦波, 杜建强, 罗计根, 聂斌, 刘勇, 邱俊洋. 基于知识表示学习的知识图谱补全研究进展[J]. 计算机工程与应用, 2023, 59(18): 59-73.
YU Mengbo, DU Jianqiang, LUO Jigen, NIE Bin, LIU Yong, QIU Junyang. Research Progress of Knowledge Graph Completion Based on Knowledge Representation Learning[J]. Computer Engineering and Applications, 2023, 59(18): 59-73.
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