摘要
为了及时了解基于TransE的表示学习方法的最新研究进展,通过归纳与整理,将基于TransE的表示学习方法分为基于复杂关系、基于关系路径、基于图像信息以及基于其他方面的方法四种类型。对每一种方法的设计思路、优缺点等进行了详细的分析,同时对基于TransE的表示学习方法的公共数据集与评价指标进行了对比、总结,对各种基于TransE的表示学习算法在实验中的表现进行了对比分析。从研究结果来看,PaSKoGE、NTransGH、TCE、TransD方法在进行链接预测和三元组分类任务上表现效果最好,值得推广和进一步拓展,并可在其特定于路径的嵌入、两层神经网络、三元组上下文、动态构造映射矩阵上进一步完善。
In order to understand the latest research progress of TransE based representation learning methods in real time,this paper classified TransE based representation learning methods into four types:the method based on complex relationship,the method based on relationship path,the method based on image information,and the method based on other aspects.Then,this paper analyzed the design ideas,advantages and disadvantages of each method.At the same time,it compared and summarized the common data sets and evaluation indexes of the TransE based representation learning method,as well as the performance of various TransE based representation learning algorithms in the experiment.From the research results,PaSKoGE,NTransGH,TCE and TransD method perform the best in link prediction and triple classification tasks,which are worth promoting and further expanding,and can be further improved in path specific embedding,two-layer neural network,triple context and dynamic mapping matrix construction.
作者
张正航
钱育蓉
行艳妮
赵鑫
Zhang Zhenghang;Qian Yurong;Xing Yanni;Zhao Xin(College of Software&Processing in Xinjiang Uygur Autonomous Region,Xinjiang University,Urumqi 830046,China;.Key Laboratory of Signal Detection&Processing in Xinjiang Uygur Autonomous Region,Xinjiang University,Urumqi 830046,China)
出处
《计算机应用研究》
CSCD
北大核心
2021年第3期656-663,共8页
Application Research of Computers
基金
国家自然科学基金资助项目(61966035)
新疆维吾尔自治区智能多模态信息处理团队资助项目(XJEDU2017T002)
新疆维吾尔自治区研究生创新项目(XJ2019G072)。
关键词
知识图谱
表示学习
TransE模型
知识图谱嵌入
翻译模型
knowledge graph(KG)
representation learning
TransE model
knowledge graph embedding(KGE)
translation model
