基于层级注意力机制的链接预测模型研究

赵晓娟; 贾焰; 李爱平; 陈恺

doi:10.11959/j.issn.1000-436x.2021057

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基于层级注意力机制的链接预测模型研究

学术论文 | 更新时间：2024-06-05

- 基于层级注意力机制的链接预测模型研究
- Research on link prediction model based on hierarchical attention mechanism
- 通信学报 2021年42卷第3期页码：36-44
- 作者机构：
  
  1. 国防科技大学计算机学院，湖南长沙 410073
  2. 湖南工业大学商学院，湖南株洲 412007
- 作者简介：
  
  [ "赵晓娟（1975- ），女，湖南娄底人，国防科技大学博士生，主要研究方向为知识图谱表示、知识图谱推理、网络空间安全。" ]
  [ "贾焰（1960- ），女，四川成都人，博士，国防科技大学教授、博士生导师，主要研究方向为数据挖掘、大数据分析、信息安全等。" ]
  [ "李爱平（1974- ），男，山东诸城人，博士，国防科技大学研究员，主要研究方向为大数据分析、数据挖掘和网络信息安全。" ]
  [ "陈恺（1994- ），男，湖北十堰人，国防科技大学硕士生，主要研究方向为知识图谱推理、网络空间安全。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB0802204);国家重点研究发展计划基金资助项目(2016QY03D0603);国家重点研究发展计划基金资助项目(2016QY03D0601);国家重点研发计划基金资助项目(2017YFB0803301);国家重点研发计划基金资助项目(2019QY1406);广东省重点研发计划基金资助项目(2019B010136003);国家自然科学基金资助项目(61732004);国家自然科学基金资助项目(61732022);国家自然科学基金资助项目(61672020);湖南省重点研发计划资助项目(2018GK2056);湖南省教育厅科研基金资助项目(19C0597)
- DOI：10.11959/j.issn.1000-436x.2021057
  中图分类号： TP391
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-25
- 稿件说明：
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赵晓娟, 贾焰, 李爱平, 等. 基于层级注意力机制的链接预测模型研究[J]. 通信学报, 2021,42(3):36-44.

Xiaojuan ZHAO, Yan JIA, Aiping LI, et al. Research on link prediction model based on hierarchical attention mechanism[J]. Journal on communications, 2021, 42(3): 36-44.
赵晓娟, 贾焰, 李爱平, 等. 基于层级注意力机制的链接预测模型研究[J]. 通信学报, 2021,42(3):36-44. DOI： 10.11959/j.issn.1000-436x.2021057.

Xiaojuan ZHAO, Yan JIA, Aiping LI, et al. Research on link prediction model based on hierarchical attention mechanism[J]. Journal on communications, 2021, 42(3): 36-44. DOI： 10.11959/j.issn.1000-436x.2021057.

摘要

为了解决已有图注意力机制在进行链接预测相关任务时，容易造成注意力分配向某些出现频率高的关系倾斜的问题，提出了一种基于层级注意力机制的链接预测模型。在链接预测任务中，通过设计分层注意力机制，根据预测任务中的关系对知识图谱中与给定实体相连的不同类型的关系给予不同的注意力。在关注多跳邻居实体特征的同时，更关注关系特征以找到符合目标关系的关系类型。在多个基准数据集上与主流模型进行对比实验，实验结果表明，所提模型性能优于主流模型，并具有较好的稳健性。

Abstract

In order to solve the problem that the existing graph attention mechanism tends to cause attention distribution to certain relations with high frequency when performing link prediction related tasks

a new link prediction model based on hierarchical attention mechanism was proposed.In the link prediction task

a hierarchical attention mechanism was designed to give different attention to the relationships of different relationship types connected to a given entity in the knowledge graph according to the relationship in the prediction task.While the characteristics of multi-hop neighbor entities were pay attention to

the relationship characteristics was pay more attention to find the relationship type that matches the target relationship.Through comparison experiments with the mainstream models on multiple benchmark data sets

the results show that the performance of the model is better than the mainstream models and has good robustness.

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references

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