摘要
为研究中韩双语实体自动对齐方法,提出了一种融合图注意力网络(GAT)和基于超平面平移的知识图谱嵌入模型(TransH)的跨语言实体对齐模型.使用中韩实体数据集对模型进行验证表明,该模型的Hits@1、Hits@5和Hits@10在韩文对齐中文时分别达到了49.62%、80.89%和91.76%,在中文对齐韩文时分别达到49.79%、80.74%和91.67%,且优于传统的基于知识嵌入或图嵌入的对齐方法.因此该模型可为构建中韩对齐知识图谱以及其他语言的对齐知识图谱提供参考.
To study the automatic alignment method of Chinese and Korean bilingual entities,a cross-language entity alignment model combining graph attention network(GAT)and knowledge graph embedding model based on hyperplane translation(TransH)is proposed.Validation of the model using the Chinese and Korean entity data sets shows that the Hits@1,Hits@5,and Hits@10 of the model reached 49.62%,80.89%and 91.76%respectively,when aligning Korean to Chinese;when Chinese is aligned with Korean,it reaches 49.79%,80.74%and 91.67%respectively.It is better than traditional alignment methods based on knowledge embedding or graph embedding.Therefore,the model can provide a reference for constructing a Chinese-Korean alignment knowledge graph and alignment knowledge graph of other languages.
作者
金城
崔荣一
赵亚慧
张振国
JIN Cheng;CUI Rongyi;ZHAO Yahui;ZHANG Zhenguo(College of Engineering,Yanbian University,Yanji 133002,China)
出处
《延边大学学报(自然科学版)》
CAS
2021年第4期356-360,共5页
Journal of Yanbian University(Natural Science Edition)
基金
延边大学校企合作项目(延大科合字[2020]15号)。
关键词
中韩跨语言实体
实体对齐
知识图谱
图注意力网络
Chinese-Korean cross language entity
entity alignment
knowledge graph
graph attention network