Network Characterization of Domain Knowledge Clusters Based on Heterogeneous Information Network
Yang Xinyi1,4,5, Yang Jianlin2,4,5, Ye Wenhao3,4,5
1.School of Journalism and Communication, Shaanxi Normal University, Xi’an 710119 2.School of Information Management, Nanjing University, Nanjing 210023 3.College of Information Management, Nanjing Agricultural University, Nanjing 210095 4.Key Laboratory of Data Engineering and Knowledge Services in Provincial Universities (Nanjing University), Nanjing 210023 5.National Security Development Research Institute of Nanjing University, Nanjing 210023
摘要多主体参与的领域知识聚类能够从宏观和微观、内容与结构等多维度展现领域知识结构，对于认知领域知识的完整体系具有重要意义。本研究利用异质信息网络建构学者、论文及期刊等多类型知识实体与关系，形成知识的异质信息网络；在网络聚类中，引入图神经网络框架，融合网络结构特征与文本内容特征学习节点向量表示，利用节点表示更新连边权重，结合网络社团检测算法和社团归并、裂变策略识别领域知识簇。最后，从文本内容和网络特征两个方面分析领域知识簇，认知领域知识构成。以数据库/数据挖掘/内容检索（database， data mining， content retrieval，DBDMIR）领域的数据集为例进行实证，本研究的聚类流程改善了聚类效果，识别了语义明确、社团结构显著的领域知识簇。领域知识簇的文本特征表述了领域内的研究主题，拓扑特征反映了知识簇的形成机制和发展情况，比如，以论文发表在期刊的关系形成的星形知识簇揭示了领域内重要期刊的研究焦点，引用关系密集的网状知识簇代表了相对成熟的方向，而引用关系稀疏、依赖作者-论文间的异质关系连通的网状知识簇代表了新兴的研究方向。簇间关联分析表明，知识簇间的偏好连接将领域知识划分为多个子领域，异质连接偏好展示了知识簇间的知识交流方式。文本和网络特征的综合分析展示了领域知识发展的全貌，展现了多主体参与的领域知识簇在预测新兴主题方面的潜力。
杨欣谊, 杨建林, 叶文豪. 基于异质信息网络的领域知识簇网络特征分析[J]. 情报学报, 2025, 44(9): 1128-1143.
Yang Xinyi, Yang Jianlin, Ye Wenhao. Network Characterization of Domain Knowledge Clusters Based on Heterogeneous Information Network. 情报学报, 2025, 44(9): 1128-1143.
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