摘要
知识图谱是用来描述世界中存在的各种实体和概念以及他们之间的关系的一种语义网络,近年来被广泛应用于智能问答、智能推荐和信息检索等领域。目前,大多数知识图谱都具有不完整性,因此,知识图谱补全成为一项重要的任务。根据模型构造方法的不同,将知识图谱补全模型分为传统知识图谱补全模型、基于神经网络的知识图谱补全模型和基于元学习的知识图谱补全模型三类,对这三种知识图谱补全模型的分类情况进行介绍;总结知识图谱补全方法所使用的数据集和评价指标,并从各个模型优点和不足等方面对各类模型进行详细的对比分析。最后,对知识图谱补全进行归纳与总结,并展望未来的研究方向。
Knowledge graph is a semantic network used to describe various entities and concepts that exist in the world,as well as their relationships.In recent years,it has been widely used in fields such as intelligent question answering,intelligent recommendation,and information retrieval.At present,most knowledge graphs are incomplete,therefore,knowledge graph completion has become an important task.Firstly,based on the different methods of model construction,knowledge graph completion models are divided into three categories:traditional knowledge graph completion models,knowledge graph completion models based on neural networks,and knowledge graph completion models based on meta learning.The classification of these three knowledge graph completion models is introduced.Then,the dataset and evaluation indicators used in the knowledge graph completion method are summarized,and detailed comparative analysis is conducted on various models from the perspectives of their advantages and disadvantages.Finally,the knowledge graph completion is summarized and summarized,and future research directions are prospected.
作者
张文豪
徐贞顺
刘纳
王振彪
唐增金
王正安
ZHANG Wenhao;XU Zhenshun;LIU Na;WANG Zhenbiao;TANG Zengjin;WANG Zheng’an(College of Compute Science and Engineering,North Minzu University,Yinchuan 750021,China;The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs Commission,North Minzu University,Yinchuan 750021,China)
出处
《计算机工程与应用》
CSCD
北大核心
2024年第12期61-73,共13页
Computer Engineering and Applications
基金
宁夏自然科学基金(2021AAC03217,2021AAC03224)
北方民族大学研究生创新项目(YCX23162)。
关键词
知识图谱
翻译模型
张量分解
神经网络
元学习
知识图谱补全
knowledge graph
translation model
tensor decomposition
neural network
meta learning
knowledge graph completion(KGC)
