Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1050-1056.DOI: 10.11772/j.issn.1001-9081.2021071227
Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)
• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles
Shoulong JIAO(), Youxiang DUAN, Qifeng SUN, Zihao ZHUANG, Chenhao SUN
Received:
2021-07-14
Revised:
2021-08-22
Accepted:
2021-08-23
Online:
2022-04-28
Published:
2022-04-10
Contact:
Shoulong JIAO
About author:
DUAN Youxiang, born in 1964, Ph. D., professor. His research interests include network and service computing, computer technology application in field of oil and gas.Supported by:
通讯作者:
焦守龙
作者简介:
段友祥(1964—),男,山东东营人,教授,博士,CCF会员,主要研究方向:网络与服务计算、计算机技术在油气领域的应用基金资助:
CLC Number:
Shoulong JIAO, Youxiang DUAN, Qifeng SUN, Zihao ZHUANG, Chenhao SUN. Knowledge representation learning method incorporating entity description information and neighbor node features[J]. Journal of Computer Applications, 2022, 42(4): 1050-1056.
焦守龙, 段友祥, 孙歧峰, 庄子浩, 孙琛皓. 融合实体描述信息和邻居节点特征的知识表示学习方法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1050-1056.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021071227
数据集 | #Ent | #Rel | #Train | #Valid | #Text |
---|---|---|---|---|---|
FB15K-237 | 14 541 | 237 | 272 115 | 17 535 | 20 466 |
WN18RR | 40 943 | 11 | 86 835 | 3 034 | 3 134 |
Tab. 1 Experimental dataset statistics
数据集 | #Ent | #Rel | #Train | #Valid | #Text |
---|---|---|---|---|---|
FB15K-237 | 14 541 | 237 | 272 115 | 17 535 | 20 466 |
WN18RR | 40 943 | 11 | 86 835 | 3 034 | 3 134 |
模型 | 准确率 | 模型 | 准确率 | ||
---|---|---|---|---|---|
FB15K-237 | WN18RR | FB15K-237 | WN18RR | ||
TransE | 66.7 | 58.5 | DKRL | 76.3 | 77.1 |
TransH | 75.1 | 73.2 | KBGAT | 78.4 | 77.5 |
TransR | 73.3 | 74.2 | BAGAT | 82.1 | 78.6 |
TransD | 69.6 | 68.8 |
Tab. 2 Accuracy of triple classification
模型 | 准确率 | 模型 | 准确率 | ||
---|---|---|---|---|---|
FB15K-237 | WN18RR | FB15K-237 | WN18RR | ||
TransE | 66.7 | 58.5 | DKRL | 76.3 | 77.1 |
TransH | 75.1 | 73.2 | KBGAT | 78.4 | 77.5 |
TransR | 73.3 | 74.2 | BAGAT | 82.1 | 78.6 |
TransD | 69.6 | 68.8 |
模型 | MR | Hits@n/% | ||
---|---|---|---|---|
@1 | @3 | @10 | ||
TransE | 330 | 19.8 | 37.6 | 44.1 |
DKRL | 217 | 20.3 | 32.7 | 47.9 |
ConvKB | 226 | 19.8 | 32.4 | 50.1 |
ConvE | 247 | 22.5 | 34.2 | 49.6 |
R-GCN | 600 | 10.0 | 18.1 | 30.0 |
KBGAT | 210 | 43.9 | 55.6 | 62.6 |
BAGAT | 153 | 45.7 | 56.0 | 66.1 |
Tab. 3 Link prediction results on FB15K-237 dataset
模型 | MR | Hits@n/% | ||
---|---|---|---|---|
@1 | @3 | @10 | ||
TransE | 330 | 19.8 | 37.6 | 44.1 |
DKRL | 217 | 20.3 | 32.7 | 47.9 |
ConvKB | 226 | 19.8 | 32.4 | 50.1 |
ConvE | 247 | 22.5 | 34.2 | 49.6 |
R-GCN | 600 | 10.0 | 18.1 | 30.0 |
KBGAT | 210 | 43.9 | 55.6 | 62.6 |
BAGAT | 153 | 45.7 | 56.0 | 66.1 |
模型 | MR | Hits@n/% | ||
---|---|---|---|---|
@1 | @3 | @10 | ||
TransE | 2 310 | 4.25 | 44.00 | 52.90 |
DKRL | 2 197 | 10.30 | 45.30 | 54.70 |
ConvKB | 1 734 | 5.73 | 44.30 | 55.30 |
ConvE | 3 457 | 38.90 | 43.50 | 53.10 |
R-GCN | 6 679 | 8.00 | 13.50 | 20.80 |
KBGAT | 1 940 | 36.10 | 48.30 | 58.10 |
BAGAT | 1 878 | 36.40 | 48.50 | 59.30 |
Tab. 4 Link prediction results on WN18RR dataset
模型 | MR | Hits@n/% | ||
---|---|---|---|---|
@1 | @3 | @10 | ||
TransE | 2 310 | 4.25 | 44.00 | 52.90 |
DKRL | 2 197 | 10.30 | 45.30 | 54.70 |
ConvKB | 1 734 | 5.73 | 44.30 | 55.30 |
ConvE | 3 457 | 38.90 | 43.50 | 53.10 |
R-GCN | 6 679 | 8.00 | 13.50 | 20.80 |
KBGAT | 1 940 | 36.10 | 48.30 | 58.10 |
BAGAT | 1 878 | 36.40 | 48.50 | 59.30 |
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