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基于力导向布局的知识图谱可视化方法

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知识图谱数据是由自然语言处理模型在海量文本文献中提取出来的、具有实体间关系的一种典型网络结构数据,而网络结构数据的主流可视化方法是使用力导向布局的节点链接图。针对传统力导向布局的节点链接图没有考虑知识图谱数据中的实体标签和关系标签信息,导致结果中存在实体节点和关系链接的分布较为随机的问题,提出一种基于力导向布局的知识图谱可视化方法。首先对知识图谱数据进行数据准备,获得规模合适的知识图谱子图数据;然后在力导向布局中加入3种新力,使得改进后的布局可以更好地展示知识图谱中实体之间的关系、实体和关系标签类型;最后引入边绑定技术并提供基本的交互技术,提升方法的可视效果和交互功能。在具有3万个实体的医疗知识图谱数据和具有200万个实体的网络黑产知识图谱数据上的实验结果表明,与其他力导向布局方法相比,所提方法的整体布局在细节上更有规律,可读性更好。
Force-Directed Layout Based Knowledge Graph Visualization Method
Knowledge graph data is a typical network data with inter-entity relationships extracted by natural language processing models from a large amount of text documents,and the major visualization method for network data is using node-link diagrams with force-directed layout.The traditional node-link diagrams with force-directed layout do not take into account the information of entity labels and relationship labels in the knowledge graph data,which leads to the problem of random distribution of entity nodes and relationship links in the layout results.Firstly,the knowledge graph data is preprocessed to obtain the knowledge graph subgraph data with appropriate size.Secondly,3 new forces are added to the force-directed layout so that the improved layout can better display the relationships between entities,entities and relationship label types in the knowledge graph.Finally,edge bundling techniques are introduced and basic interaction techniques are provided to enhance the visualization and interaction of the method.Compared with other force-directed layout methods,the layout result is more regular in details and more readable on medical knowledge graph data with 30 thousand entities and the cyber blackmail knowledge graph data with 2 million entities.

knowledge graphvisualizationforce-directed layoutedge bundling

康健梓、周虹

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深圳大学计算机与软件学院 深圳 518052

知识图谱 可视化 力导向布局 边绑定

国家自然科学基金青年科学基金深圳海关科研项目

611030552024SZHK001

2024

计算机辅助设计与图形学学报
中国计算机学会

计算机辅助设计与图形学学报

CSTPCD北大核心
影响因子:0.892
ISSN:1003-9775
年,卷(期):2024.36(8)