基于知识图谱表示学习的谣言早期检测方法

皮德常; 吴致远; 曹建军

doi:10.12263/DZXB.20210121

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基于知识图谱表示学习的谣言早期检测方法

学术论文 | 更新时间：2025-12-08

- 基于知识图谱表示学习的谣言早期检测方法
- Early Rumor Detection Method Based on Knowledge Graph Representation Learning
- 电子学报 2023年51卷第2期页码：385-395
- 作者机构：
  
  1.南京航空航天大学计算机科学与技术学院，江苏南京 211106
  2.中国人民解放军国防科技大学第六十三研究所，江苏南京 210007
- 作者简介：
  
  [ "皮德常男，1971年11月生. 南京航空航天大学教授、博士生导师.主要研究方向为大数据、文本挖掘和隐私保护.E-mail: dc.pi@nuaa.edu.cn" ]
  [ "吴致远男，1996年6月生.南京航空航天大学硕士研究生.主要研究方向为文本挖掘、图神经网络.E-mail: aguiller2018@163.com" ]
  [ "曹建军（通讯作者）男，1975年6月生.国防科技大学第六十三研究所副研究员、硕士生导师.主要研究方向为数据清洗、情报分析.E-mail: jianjuncao@yeah.net" ]
- 基金信息：
  
  科技创新2030—“新一代人工智能”重大项目(2021ZD0113103)
- DOI：10.12263/DZXB.20210121
  中图分类号： TP393;TP309
- 收稿：2021-01-18，
  
  修回：2021-06-08，
  
  纸质出版：2023-02-25
- 稿件说明：
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皮德常,吴致远,曹建军.基于知识图谱表示学习的谣言早期检测方法[J].电子学报,2023,51(02):385-395.

PI De-chang,WU Zhi-yuan,CAO Jian-jun.Early Rumor Detection Method Based on Knowledge Graph Representation Learning[J].ACTA ELECTRONICA SINICA,2023,51(02):385-395.
皮德常,吴致远,曹建军.基于知识图谱表示学习的谣言早期检测方法[J].电子学报,2023,51(02):385-395. DOI： 10.12263/DZXB.20210121.

PI De-chang,WU Zhi-yuan,CAO Jian-jun.Early Rumor Detection Method Based on Knowledge Graph Representation Learning[J].ACTA ELECTRONICA SINICA,2023,51(02):385-395. DOI： 10.12263/DZXB.20210121.

摘要

社交网络谣言是严重危害社会安全的一个重要问题.目前的谣言检测方法基本上都依赖用户评论数据.为了获取可供模型训练的足量评论数据，需要任由谣言在社交平台上传播一段时间，这就扩大了谣言的危害.本文提出了一种基于知识图谱表示学习的谣言检测方法.该方法不依赖用户评论数据.首先基于PN-KG2REC算法得到实体和关系的表示；然后将待检测三元组中的实体和关系表示进行拼接，得到三元组表示；最后对三元组的向量表示进行分类，并根据分类结果判断待检测三元组描述内容的真假性.采用公开数据的实验结果表明，本文提出的谣言检测方法在不依赖用户评论数据的前提下，能够有效地对谣言进行早期检测.

Abstract

Rumor on social network is an important issue that seriously harms social security. At present

most machine learning based rumor detection methods use comment data as auxiliary information for rumor detection

aiming to achieve higher accuracy. However

in order to obtain enough comment data for training models

it is inevitable that rumors will spread on social platforms for a period of time

which further expands the harm. This paper proposes a rumor detection method based on knowledge graph representation learning. This method uses the facts in the knowledge graph to detect rumors instead of relying on comment data. This method first obtains the representations of entities and relations based on proposed algorithm PN-KG2REC; and then concatenates the representations of entity and relation to obtain the representations of triplets; finally

it determines the authenticity of the contents described by the triplets according to the classification results. Experimental results show that the proposed method can effectively detect rumors without relying on comment posts in the early.

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references

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