计算机应用与软件2024，Vol.41Issue(8)：345-350,6.DOI:10.3969/j.issn.1000-386x.2024.08.049
TransE-KCB:一种改进负样本采样的知识图谱表示方法
TRANSE-KCB:AN IMPROVED KNOWLEDGE GRAPH REPRESENTATION METHOD FOR NEGATIVE SAMPLE SAMPLING
徐金诚 1葛云生1
作者信息
- 1. 桂林理工大学信息科学与工程学院 广西桂林 541006
- 折叠
摘要
Abstract
In order to solve the shortcomings of randomly generated negative samples in the translation model,to generate high-quality negative samples and improve the training effect of the model,the paper proposes an improved knowledge representation learning model for negative sample sampling,which is called TransE-KCB.The model introduced the K-Means++clustering algorithm to form different types of similarity entity clusters.5 entities in the cluster were randomly selected,and the similarity with the replaced entity was calculated.The highest ranked entity was selected and replaced with the replaced entity.On this basis,in order to solve the problem of"false negatives",this paper introduced a Bloom filter to filter"false negatives".The experimental results show that,compared with TransE and other models,the TransE-KCB model has better model expression ability,and the knowledge representation ability has been greatly improved.关键词
负样本/翻译模型/三元组分类/知识表示Key words
Negative sample/Translation model/Triplet classification/Knowledge representation分类
信息技术与安全科学引用本文复制引用
徐金诚,葛云生..TransE-KCB:一种改进负样本采样的知识图谱表示方法[J].计算机应用与软件,2024,41(8):345-350,6.
