[1]饶官军,古天龙,常亮,等.基于相似性负采样的知识图谱嵌入[J].智能系统学报,2020,15(2):218-226.[doi:10.11992/tis.201811022]
 RAO Guanjun,GU Tianlong,CHANG Liang,et al.Knowledge graph embedding based on similarity negative sampling[J].CAAI Transactions on Intelligent Systems,2020,15(2):218-226.[doi:10.11992/tis.201811022]
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基于相似性负采样的知识图谱嵌入(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年2期
页码:
218-226
栏目:
学术论文—知识工程
出版日期:
2020-07-05

文章信息/Info

Title:
Knowledge graph embedding based on similarity negative sampling
作者:
饶官军 古天龙 常亮 宾辰忠 秦赛歌 宣闻
桂林电子科技大学 广西可信软件重点实验室, 广西 桂林 541004
Author(s):
RAO Guanjun GU Tianlong CHANG Liang BIN Chenzhong QIN Saige XUAN Wen
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
关键词:
知识图谱表示学习随机抽样相似性负采样K-Means聚类随机梯度下降链接预测三元组分类
Keywords:
knowledge graphrepresentation learningrandom samplingsimilarity samplingK-means clusteringstochastic gradient descentlink predictiontriple classification
分类号:
TP391
DOI:
10.11992/tis.201811022
摘要:
针对现有知识图谱嵌入模型通过从实体集中随机抽取一个实体来生成负例三元组,导致负例三元组质量较低,影响了实体与关系的特征学习能力。研究了影响负例三元组质量的相关因素,提出了基于实体相似性负采样的方法来生成高质量的负例三元组。在相似性负采样方法中,首先使用K-Means聚类算法将所有实体划分为多个组,然后从正例三元组中头实体所在的簇中选择一个实体替换头实体,并以类似的方法替换尾实体。通过将相似性负采样方法与TransE相结合得到TransE-SNS。研究结果表明:TransE-SNS在链路预测和三元组分类任务上取得了显著的进步。
Abstract:
For the existing knowledge graph embedding model, the random extraction of an entity from the entity set results in the generation of lower-quality negative triples, and this affects the feature learning ability of the entity and the relationship. In this paper, we study the related factors affecting the quality of negative triples, and propose an entity similarity negative sampling method to generate high-quality negative triples. In the similarity negative sampling method, all entities are first divided into a number of groups using the K-means clustering algorithm. Then, corresponding to each positive triple, an entity is selected to replace the head entity from the cluster, whereby the head entity is located in the positive triple, and the tail entity is replaced in a similar approach. TransE-SNS is obtained by combining the similarity negative sampling method with TransE. Experimental results show that TransE-SNS has made significant progress in link prediction and triplet classification tasks.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-12-04。
基金项目:国家自然科学基金资助项目(U1501252,61572146);广西创新驱动重大专项项目(AA17202024);广西自然科学基金项目(2016GXNSFDA380006);广西高校中青年教师基础能力提升项目(2018KYD203);广西研究生教育创新计划项目(YCSW2018139)
作者简介:饶官军,硕士研究生,主要研究方向为知识图谱、表示学习;古天龙,教授,博士生导师,主要研究方向为形式化方法、知识工程与符号推理、协议工程与移动计算、可信泛在网络、嵌入式系统。主持国家863计划项目、国家自然科学基金项目、国防预研重点项目、国防预研基金项目等30余项,出版学术著作3部,发表学术论文130余篇;常亮,教授,博士,中国计算机学会高级会员,主要研究方向为数据与知识工程、形式化方法、智能系统。主持并完成国家自然科学基金项目1项、广西省自然科学基金项目1项。发表学术论文70余篇
通讯作者:宾辰忠.E-mail:cz_bin@guet.edu.cn
更新日期/Last Update: 1900-01-01