基于多视角IB的安全风险关系抽取研究  

Study on Security Risk Relation Extraction Based on Multi-view IB

作　　者：李希望 曹培松 吴俞颖 郭淑明 佘维[1,2] LI Xiwang;CAO Peisong;WU Yuying;GUO Shuming;SHE Wei(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450000,China;Songshan Laboratory,Zhengzhou 450000,China;National Digital Switching System Engineering&Technological R&D Center,Zhengzhou 450000,China)

机构地区：[1]郑州大学网络空间安全学院,郑州450000 [2]嵩山实验室,郑州450000 [3]国家数字交换系统工程技术研究中心,郑州450000

出　　处：《计算机科学》2025年第5期330-336,共7页Computer Science

基　　金：国家重点研发计划(31703-3);嵩山实验室预研项目(YYYY022022003)。

摘　　要：安全风险管理是保障安全的核心任务,传统识别安全风险的方法已经不能满足智能化发展的需求。关系抽取是安全风险识别的方法之一,研究关系抽取对安全风险管理具有重要意义。尽管现有的模型已经取得了较好的性能,但是大多数现有的关系抽取模型忽略了领域实体表征不足的问题,并且数据中存在较多不相关信息。针对该问题,提出了一个基于多视角IB(Information Bottleneck)的安全风险关系抽取模型MIBRE(Multi-view Information Bottleneck for Relation Extraction),它通过融合多视角语义信息来达到增强领域实体语义的目的。这两个视角分别是文本视角和图像视角。为了最大化获取两个视角之间的相关信息,基于信息瓶颈方法构造了一个目标函数,在压缩两个视角信息的同时最大化地保留了相关信息。在两个真实的铁路领域数据集上的实验表明,MIBRE识别的F1值分别达到了64.28%和74.34%,相较于基于异构图的LGGCN模型F1值分别提升了4.41%和2.98%,相较于基于注意力机制的TDGAT模型F1值分别提升了1.89%和1.53%。实验结果验证了所提模型在安全风险识别上的有效性。Safety risk management is the core assignment to ensure safety,and the traditional methods of identifying safety risks can no longer meet the needs of intelligent development.Research on relation extraction is of significant importance for security risk management,as it serves as one of the methods for identifying security risks.However,most existing relation extraction models ignore the problem of insufficient representation of domain entity and contain more noise in the data.To address the above problems,a multi-view IB-based safety risk relation extraction model(MIBRE)is proposed.Specifically,it achieves enhanced domain entity semantics by fusing semantic information from multi-view.In order to obtain the maximum relevant information between the two views,an objective function is constructed using the information bottleneck approach.The relevant information is maximally preserved and restored while compressing the information between the two views.Experiments on two real domain datasets show that the F1 value recognized by MIBRE reaches 64.28%and 74.34%respectively,which is 4.41%and 2.98%higher than that of LGGCN based on heterogeneous graph model.Compared with TDGAT based on attention mechanism model,F1 value increased by 1.89%and 1.53%respectively.The effectiveness of the proposed model in security risk identification is verified by experiments.

关 键 词：关系抽取 信息瓶颈 多视角 安全风险 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]