邻域双向聚合与全局感知的TKG链接预测模型

doi:10.11896/jsjkx.220900061

计算机科学 ›› 2023, Vol. 50 ›› Issue (8): 177-183.doi: 10.11896/jsjkx.220900061

邻域双向聚合与全局感知的TKG链接预测模型

唐绍赛, 申德荣, 寇月, 聂铁铮

东北大学计算机科学与工程学院沈阳 110167

收稿日期:2022-09-07 修回日期:2022-12-26 出版日期:2023-08-15 发布日期:2023-08-02
通讯作者: 申德荣(shendr@mail.neu.edu.cn)
作者简介:(1533026814@qq.com)
基金资助:
国家自然科学基金(62172082,62072084,62072086);中央高校基本科研业务费(N2116008)

Link Prediction Model on Temporal Knowledge Graph Based on Bidirectionally Aggregating Neighborhoods and Global Aware

TANG Shaosai, SHEN Derong, KOU Yue, NIE Tiezheng

School of Computer Science and Engineering,Northeastern University,Shenyang 110167,China

Received:2022-09-07 Revised:2022-12-26 Online:2023-08-15 Published:2023-08-02
About author:TANG Shaosai,born in 1998,postgra-duate.His main research interest is link prediction on temporal knowledge graph.
SHEN Derong,born in 1964,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include distributed database and web data management.
Supported by:
National Natural Science Foundation of China(62172082,62072084,62072086) and Fundamental Research Funds for the Central Universities(N2116008).

摘要/Abstract

摘要： 时序知识图谱(Temporal Knowledge Graph,TKG)在推荐系统、搜索引擎和自然语言处理等领域有着广泛的应用前景,然而其不完备性限制了它的应用,因此研究面向TKG的链接预测模型具有重要作用。针对已有的工作大多面向TKG补全,无法预测未来的事实,提出了一种邻域双向聚合与全局感知的TKG链接预测模型。一方面,分别聚合实体的主动和被动行为并通过循环神经网络建模其历时演变来捕捉实体的短期行为;另一方面,基于全局感知模块来捕捉实体的长期行为。在4个基准数据集上进行了测试,结果表明所提模型能够提升模型预测未来事实的性能。

关键词: 时序知识图谱, 链接预测, 循环神经网络

Abstract: Temporal knowledge graphs(TKG) have great potential of application in many fields,such as recommender systems,search engine and natural language processing,but the incompleteness of TKG limites its application,so it is important to study link prediction model on TKG.Most existing methods focus on TKG completion and can't predict future facts.This paper proposes a link prediction model on TKG,which is based on bidirectionally aggregating neighborhoods and global aware.On the one hand,the proposed model independently aggregates entity's recently active and positive behavior and models their temporal evolution by recurrent neural network (RNN).On the other hand,it captures the chronic behavior patterns of entities by global aware module.Experimental results on four benchmark datasets show that our proposed method can improve the performance of forecasting future facts.

Key words: Temporal knowledge graph, Link prediction, Recurrent neural network

中图分类号:

TP391

唐绍赛, 申德荣, 寇月, 聂铁铮. 邻域双向聚合与全局感知的TKG链接预测模型[J]. 计算机科学, 2023, 50(8): 177-183. https://doi.org/10.11896/jsjkx.220900061

TANG Shaosai, SHEN Derong, KOU Yue, NIE Tiezheng. Link Prediction Model on Temporal Knowledge Graph Based on Bidirectionally Aggregating Neighborhoods and Global Aware[J]. Computer Science, 2023, 50(8): 177-183. https://doi.org/10.11896/jsjkx.220900061

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邻域双向聚合与全局感知的TKG链接预测模型

Link Prediction Model on Temporal Knowledge Graph Based on Bidirectionally Aggregating Neighborhoods and Global Aware

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