摘要
知识重组在推动创新和跨学科融合中发挥着关键作用,现有研究在实现其早期预测时多依赖于同构知识网络,难以刻画知识元与其关联实体间的复杂关系,限制了预测性能的提升。为此,本研究提出一种基于异构图神经网络的知识重组预测框架,引入与知识元密切相关的多种异质实体及关系,通过多种连接策略构建异构知识网络,并结合能够感知边类型的关系图卷积神经网络实现对知识重组的预测。通过对肿瘤免疫治疗领域的实证研究表明,本框架的预测性能全面优于传统同构预测框架,其中F1值从0.706提升至0.889。同时,实验验证了异质节点连接策略对预测效果的显著影响。
Knowledge recombination is pivotal for fostering innovation and interdisciplinary integration.Existing studies typically rely on homogenous knowledge networks for its early prediction,which fail to capture the intricate relationships between knowledge units and their associated entities,thereby constraining predictive performance.To address this limitation,this paper proposes a knowledge-recombination prediction framework based on heterogeneous graph neural networks.The framework integrates multiple heterogeneous entities and relations closely related to knowledge units,constructs an enriched heterogeneous knowledge network through diverse connection strategies,and employs a relation-aware graph convolutional network to predict potential recombination links.Empirical experiments in the cancer immunotherapy domain demonstrate that the proposed framework markedly outperforms traditional homogenous-network baselines,with the F1 score rising from 0.706 to 0.889.The results also confirm that connection strategies for heterogeneous nodes significantly influence predictive performance,underscoring the importance of heterogenous network design in knowledge-recombination prediction.
作者
任安兴
杨冠灿
行佳鑫
张滋荷
Ren Anxing;Yang Guancan;Xing Jiaxin;Zhang Zihe(School of Information Resource Management,Renmin University of China,Beijing,100872)
出处
《信息资源管理学报》
2025年第6期129-142,共14页
Journal of Information Resources Management
基金
国家自然科学基金面上项目“复杂动态视角下的技术会聚形成机理及预测方法研究”(72274205)研究成果之一。
关键词
知识重组预测
异构图神经网络
链路预测
复杂网络
多元关系
Knowledge recombination prediction
Heterogeneous graph neural network
Link prediction
Complex networks
Multiple relationship
