<bold>Smap</bold>：基于文献语义的学科知识图景可视化

doi:10.3772/j.issn.1000-0135.2023.01.007

情报学报

2023, Vol. 42

Issue (1): 74-89 DOI: 10.3772/j.issn.1000-0135.2023.01.007

情报技术与应用

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Smap：基于文献语义的学科知识图景可视化

张爽^1,2, 刘非凡^1,2, 罗双玲³, 夏昊翔^1,2

1.大连理工大学系统工程研究所，大连 116024
2.大连理工大学大数据与智能决策研究中心，大连 116024
3.大连海事大学航运经济与管理学院，大连 116026

Smap: Visualization of Scientific Knowledge Landscape Based on Document Semantics

Zhang Shuang^1,2, Liu Feifan^1,2, Luo Shuangling³, Xia Haoxiang^1,2

1.Institute of Systems Engineering, Dalian University of Technology, Dalian 116024
2.Research Center for Big Data and Intelligent Decision-Making, Dalian University of Technology, Dalian 116024
3.School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026

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摘要随着文献爆炸式增长，学科领域不断交叉融合，科研规模扩大和知识体系复杂性日益提升，如何清晰地可视化学科知识图景，进而把握知识结构和研究态势，引起了科技情报人员的广泛关注。本研究基于文档表示学习和流形学习算法，提供了一种科学领域语义地图（semantic map，Smap）构建方法。首先以Doc2Vec捕获文献间的高维语义特征，然后利用UMAP（uniform manifold approximation and projection）对文献语义临近性进行非线性降维，最后以核密度估计根据文献分布异质性刻画领域知识结构。在实证分析阶段，本研究对文献规模覆盖了从千级到百万级的4个学科领域，进行了领域可视化、知识层级结构识别以及动态演化分析。进而，本研究借助引用关系、关键词以及数据集的分类体系，通过量化Smap地图上文献分布的局部纯粹性以及全局地图距离和研究差异的相关性，验证了所提方法的有效性。本研究通过与随机实验对比，进一步地量化了有效性的显著程度。本研究为当前科学领域可视化方法提供了有益补充，可为大规模科技文献数据驱动的科技情报服务提供分析工具。

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	张爽
	刘非凡
	罗双玲
	夏昊翔

关键词 ：语义地图, 知识结构可视化, 深度学习, 流形学习

收稿日期: 2021-12-06

基金资助:国家自然科学基金面上项目“协同创新网络中的集体智能动态机理研究”（71871042）；教育部人文社科规划项目“综合数据解析和动力学建模的科研协作系统演化模式与机理研究”（18YJA630118）；辽宁省社会科学规划基金项目“大数据背景下图书馆科技情报知识服务的支撑方法研究”（L16BTQ003）。

作者简介: 张爽，女，1994年生，博士研究生，研究方向为机器学习、社会网络分析、科技情报分析；刘非凡，男，1991年生，博士，研究方向为复杂网络、机器学习、科技情报分析；罗双玲，女，1978年生，副教授，硕士生导师，研究方向为知识管理、群体行为；夏昊翔，男，1972年生，教授，博士生导师，研究方向为复杂系统、计算社会科学、知识系统工程，E-mail：hxxia@dlut.edu.cn；

引用本文:

张爽, 刘非凡, 罗双玲, 夏昊翔. Smap：基于文献语义的学科知识图景可视化[J]. 情报学报, 2023, 42(1): 74-89.
Zhang Shuang, Liu Feifan, Luo Shuangling, Xia Haoxiang. Smap: Visualization of Scientific Knowledge Landscape Based on Document Semantics. 情报学报, 2023, 42(1): 74-89.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2023.01.007 或 https://qbxb.istic.ac.cn/CN/Y2023/V42/I1/74

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