基于密度聚类算法改进的语义主路径分析方法研究

doi:10.3772/j.issn.1000-0135.2024.03.004

情报学报

2024, Vol. 43

Issue (3): 287-301 DOI: 10.3772/j.issn.1000-0135.2024.03.004

情报理论与方法

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基于密度聚类算法改进的语义主路径分析方法研究

陈亮¹, 余池¹, 尚玮姣², 许海云³, 吕世炅¹, 陈利利¹

1.中国科学技术信息研究所，北京 100038
2.中国林业科学研究院林业科技信息研究所，北京 100091
3.山东理工大学管理学院，淄博 255000

Research on Improving the Semantic Main Path Analysis Method by Leveraging the Density Peak Clustering Algorithm

Chen Liang¹, Yu Chi¹, Shang Weijiao², Xu Haiyun³, Lyu Shijiong¹, Chen Lili¹

1.Institute of Scientific and Technical Information of China, Beijing 100038
2.Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091
3.Business School, Shandong University of Technology, Zibo 255000

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摘要语义主路径分析方法在改进传统主路径分析法中主路径内容单一、主题一致性较差等不足的同时，留下了两个缺陷，即所选主路径在语义空间的位置可能偏离主题聚簇中心、不同主路径的主题区分度并不明显。本文在语义主路径分析方法的基础上，提出一种逐步优化的主路径选择方法，即将主题聚簇密度和路径遍历权重进行叠加形成复合密度，通过调节复合密度中两个要素的比重来优化主题聚簇中心的定位，当聚簇中心的位置变化收敛后，将位于不同主题聚簇中心的路径作为结果输出。将本文方法分别用于电动汽车锂离子电池专利引文网络和材料科学领域高影响力论文引文网络，实验结果显示，本文方法所产生的多条主路径不仅在主题聚簇中的布局更加合理，而且选取不当主路径的可能性也大大降低，从而验证了本文方法的有效性。

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	陈亮
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	吕世炅
	陈利利

关键词 ：语义主路径分析, 主题一致性, 主题聚类, 材料科学, 电动汽车锂离子电池

收稿日期: 2022-09-05

基金资助:中央级公益性科研院所基本科研业务项目“开源科技情报智能分析系统与应用场景建设”（ZD2023-13）；国家电网公司总部科技项目“全球煤油气电耦合下我国能源安全风险识别与战略路径优化技术研究”（1400-202357341A-1-1-ZN）。

作者简介: 陈亮，男，1982年生，博士，副研究员，硕士生导师，研究方向为科技大数据与智能算法研究及应用；余池，女，2000年生，硕士研究生，研究方向为专利挖掘与机器学习；尚玮姣，女，1985年生，硕士，工程师，研究方向为科学计量学与科技评价；许海云，女，1982年生，博士，教授，博士生导师，研究方向为情报计量学的理论与实践；吕世炅，男，1976年生，硕士，工程师，研究方向为智能情报分析方法研究与应用；陈利利，通信作者，女，1998年生，硕士研究生，研究方向为专利挖掘与机器学习，E-mail：1443309803@qq.com；

引用本文:

陈亮, 余池, 尚玮姣, 许海云, 吕世炅, 陈利利. 基于密度聚类算法改进的语义主路径分析方法研究[J]. 情报学报, 2024, 43(3): 287-301.
Chen Liang, Yu Chi, Shang Weijiao, Xu Haiyun, Lyu Shijiong, Chen Lili. Research on Improving the Semantic Main Path Analysis Method by Leveraging the Density Peak Clustering Algorithm. 情报学报, 2024, 43(3): 287-301.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2024.03.004 或 https://qbxb.istic.ac.cn/CN/Y2024/V43/I3/287

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