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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (10): 1915-1922    DOI: 10.3785/j.issn.1008-973X.2023.10.001
计算机技术、自动化技术     
结合静态事实和重复历史事实的动态知识图谱推理方法
林栋1(),李永强1,*(),仇翔1,冯远静1,谢碧峰2
1. 浙江工业大学 信息工程学院，浙江 杭州 310012
2. 杭州峰景科技有限公司，浙江 杭州 310000
Dynamic knowledge graph inference method combining static facts and repeated historical facts
Dong LIN1(),Yong-qiang LI1,*(),Xiang QIU1,Yuan-jing FENG1,Bi-feng XIE2
1. School of Information Engineering, Zhejiang University of Technology, Hangzhou 310012, China
2. Hangzhou Fengjing Technology Company, Hangzhou 310000, China
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摘要：

针对现有的动态知识图谱推理方法容易忽略动态知识图谱中存在着大量静态信息和重复历史事实的问题，提出结合静态事实和重复历史事实的动态知识图谱网络方法. 该方法利用动态知识图谱中实体间隐藏的静态联系来构成静态事实，并协助动态知识图谱推理；利用历史事实构建历史词表，在预测未来时对历史词表进行查询；对历史中未发生的事实进行惩罚，提高重复历史事实的预测概率. 在2个公开的数据集上进行动态知识图谱推理实验，对比实验时选用目前主流的5个模型作为基线. 在实体预测实验中，平均倒数排名(MRR)达到0.489 1和0.530 3，Hits@10达到0.588 7和0.616 5，证明了所提方法的有效性.
关键词： 动态知识图谱;  静态事实;  重复历史事实;  历史惩罚;  实体预测    
Abstract:

A static-historical network (Sta-HisNet) method combining static facts and repeating historical facts was proposed, aiming at the problem that existing dynamic knowledge graph reasoning methods tend to overlook the vast amount of static information and repeating historical facts present in the dynamic knowledge graphs. The hidden static connections between entities in the dynamic knowledge graph were used to form static facts, assisting in the inference of the dynamic knowledge graph. Historical facts were employed to construct a historical vocabulary, and the historical vocabulary was queried when predicting the future. Facts that had not occurred in history were punished, and the probability of predicting duplicate historical facts was increased. Experiments were conducted on two public datasets for dynamic knowledge graph reasoning. Comparative experiments were performed using five mainstream models as baselines. In entity prediction experiments, the mean reciprocal rank (MRR) was 0.489 1 and 0.530 3, and Hits@10 reached 0.588 7 and 0.616 5 respectively, demonstrating the effectiveness of the proposed method.
Key words: dynamic knowledge graph    static facts    repeating historical facts    history punishment    entity prediction
收稿日期: 2022-10-17 出版日期: 2023-10-18
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目（62073294）；浙江省自然科学基金资助项目（LZ21F030003）
通讯作者: 李永强     E-mail: 332864925@qq.com;yqli@zjut.edu.cn
作者简介: 林栋（1998—），男，硕士生，从事知识图谱推理研究. orcid.org/0000-0003-0706-2020. E-mail： 332864925@qq.com
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林栋
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引用本文:

林栋,李永强,仇翔,冯远静,谢碧峰. 结合静态事实和重复历史事实的动态知识图谱推理方法[J]. 浙江大学学报(工学版), 2023, 57(10): 1915-1922.

Dong LIN,Yong-qiang LI,Xiang QIU,Yuan-jing FENG,Bi-feng XIE. Dynamic knowledge graph inference method combining static facts and repeated historical facts. Journal of ZheJiang University (Engineering Science), 2023, 57(10): 1915-1922.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.10.001    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I10/1915

图 1  Sta-HisNet框架图
图 2  ICEWS18数据集的部分静态知识图谱
图 3  Sta-HisNet的历史模式流程图
数据集 实体数 关系数 训练集数 验证集数 测试集数
ICEWS18 23 033 256 373 018 45 995 69 514
GDELT 7 691 240 1734 399 238 765 305 241
表 1  不同知识图谱在公开数据集中的数量统计
模型 MRR Hits@1 Hits@3 Hits@10
ConvE 0.366 7 0.285 1 0.398 0 0.506 9
RE-NET 0.429 3 0.361 9 0.454 7 0.558 0
RE-GCN 0.463 1 0.391 2 0.497 3 0.569 3
CyGNET 0.466 9 0.405 8 0.498 2 0.571 4
CEN 0.472 6 0.418 6 0.506 1 0.579 1
Sta-HisNet 0.489 1 0.429 4 0.515 3 0.588 7
表 2  不同模型在ICEWS18数据集上的实体预测结果
模型 MRR Hits@1 Hits@3 Hits@10
ConvE 0.359 9 0.270 5 0.393 2 0.494 4
RE-NET 0.401 2 0.324 3 0.434 0 0.538 0
RE-GCN 0.481 4 0.421 6 0.523 7 0.583 5
CyGNET 0.509 2 0.445 3 0.546 9 0.609 9
CEN 0.516 8 0.457 6 0.549 7 0.612 3
Sta-HisNet 0.530 5 0.475 5 0.560 1 0.616 5
表 3  不同模型在GDELT数据集上的实体预测结果
模型 MRR Hits@1 Hits@3 Hits@10
Sta-HisNet-NON-EMB 0.479 4 0.421 2 0.506 3 0.573 5
Sta-HisNet-NON-STA 0.482 2 0.494 5 0.509 8 0.578 1
Sta-HisNet-NON-CONV 0.473 1 0.411 3 0.503 2 0.571 3
Sta-HisNet-NON-LSTM 0.463 9 0.402 1 0.498 6 0.574 2
Sta-HisNet-NON-PUN 0.458 9 0.396 5 0.483 8 0.559 2
Sta-HisNet 0.489 1 0.429 4 0.515 3 0.588 7
表 4  各模块在 ICEWS18 数据集上的消融实验结果
模型 MRR Hits@1 Hits@3 Hits@10
Sta-HisNet-NON-EMB 0.526 3 0.471 0 0.555 2 0.611 2
Sta-HisNet-NON-STA 0.518 6 0.463 6 0.546 9 0.603 9
Sta-HisNet-NON-CONV 0.523 2 0.469 1 0.556 6 0.610 5
Sta-HisNet-NON-LSTM 0.514 5 0.459 8 0.546 3 0.601 2
Sta-HisNet-NON-PUN 0.509 2 0.445 3 0.536 9 0.589 9
Sta-HisNet 0.530 5 0.475 5 0.560 1 0.616 5
表 5  各模块在 GDELT 数据集上的消融实验结果
图 4  2种方法在2个数据集上的最佳轮数对比
图 5  3种方法在2个数据集上的运行一个回合的所需时间
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