|
|
|
| Evolution of Domain Knowledge Based on Heterogeneous Information Networks |
| Yang Xinyi1,4,5, Yang Jianlin2,4,5, Wang Wei3,4,5 |
1.School of Journalism and Communication, Shaanxi Normal University, Xi’an 710119
2.School of Information Management, Nanjing University, Nanjing 210023
3.School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003
4.National Security Development Research Institute of Nanjing University, Nanjing 210023
5.Key Laboratory of Data Engineering and Knowledge Services in Provincial Universities (Nanjing University), Nanjing 210023 |
|
|
|
|
Abstract A heterogeneous information network can model the complex relationships between multiple types of knowledge entities, thereby providing advantages in exploring the dynamics of knowledge differentiation, reorganization, integration, and evolution. This study first constructed a heterogeneous information network by extracting multiple types of relationships, such as author-paper, paper-venue, and paper-paper, and then built snapshot networks according to the evolution cycle of domain knowledge. Subsequently, the Leiden algorithm was used to detect network communities representing clusters of domain knowledge, and the evolutionary path was identified based on a model for group evolution discovery. Finally, the patterns of domain knowledge evolution were explored in terms of textual content and network topology. An empirical analysis of databases, data mining, and content retrieval demonstrated that knowledge clusters represented by heterogeneous information network communities are indicative of research topic transformation. In addition, as quantified by topological metrics including node degree centrality, k-core decomposition, and betweenness centrality, these clusters offer a nuanced interpretation of the importance of knowledge communication and maturity. The domain is advancing toward specialized applications and user personalization.
|
|
Received: 15 December 2024
|
|
|
|
1 Andrei V, Arandjelovi? O. Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance[J]. EURASIP Journal on Bioinformatics and Systems Biology, 2016, 2016(1): Article No.16.
2 Jung S, Yoon W C. An alternative topic model based on common interest authors for topic evolution analysis[J]. Journal of Informetrics, 2020, 14(3): 101040.
3 Li N, He H X. Topic evolution analysis for omics data integration in cancers[J]. Frontiers in Cell and Developmental Biology, 2021, 9: 631011.
4 宋凯, 陈悦. 技术主题动态演化分析的一种新方法: DPL-BMM模型[J]. 情报学报, 2024, 43(1): 25-33.
5 Wang X G, Cheng Q K, Lu W. Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks[J]. Scientometrics, 2014, 101(2): 1253-1271.
6 陈翔, 黄璐, 倪兴兴, 等. 基于动态语义网络分析的主题演化路径识别研究[J]. 情报学报, 2021, 40(5): 500-512.
7 Xu H Y, Luo R, Winnink J, et al. A methodology for identifying breakthrough topics using structural entropy[J]. Information Processing & Management, 2022, 59(2): 102862.
8 Zhou W, Huang H, Shi R Z, et al. Temporal heterogeneous information network embedding via semantic evolution[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12): 13031-13042.
9 Sun Y Z, Tang J, Han J W, et al. Community evolution detection in dynamic heterogeneous information networks[C]// Proceedings of the Eighth Workshop on Mining and Learning with Graphs. New York: ACM Press, 2010: 137-146.
10 Sun Y Z, Tang J, Han J W, et al. Co-evolution of multi-typed objects in dynamic star networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(12): 2942-2955.
11 李雪思, 张智雄, 王宇飞, 等. 领域知识演化分析方法综述[J]. 数据分析与知识发现, 2024, 8(1): 1-15.
12 Wang Z Y, Chen J, Chen J P, et al. Identifying interdisciplinary topics and their evolution based on BERTopic[J]. Scientometrics, 2024, 129(11): 7359-7384.
13 曹玲静, 张志强. 二十一世纪以来美国科技政策主题分析及发展态势研判[J]. 情报学报, 2024, 43(5): 616-632.
14 冉从敬, 田文芳, 贾志轩. 技术生命周期视域下专利技术主题演化分析方法研究——以视频图像处理技术领域为例[J]. 情报理论与实践, 2024, 47(9): 124-133.
15 王伟, 梁继文, 杨建林. 基于语义加权网络的重点领域科学基金资助态势识别研究[J]. 现代情报, 2025, 45(1): 46-59, 111.
16 Kim E H J, Jeong Y K, Kim Y, et al. Exploring scientific trajectories of a large-scale dataset using topic-integrated path extraction[J]. Journal of Informetrics, 2022, 16(1): 101242.
17 Fu H L, Wang M M, Li P, et al. Tracing knowledge development trajectories of the Internet of Things domain: a main path analysis[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6531-6540.
18 Fu Z M, Cao Y, Zhao Y. Identifying knowledge evolution in computer science from the perspective of academic genealogy[J]. Journal of Informetrics, 2024, 18(2): 101523.
19 王康, 陈悦, 苏成, 等. 多维视角下科学主题演化分析框架[J]. 情报学报, 2021, 40(3): 297-307.
20 Gupta M, Aggarwal C C, Han J W, et al. Evolutionary clustering and analysis of bibliographic networks[C]// Proceedings of 2011 International Conference on Advances in Social Networks Analysis and Mining. Piscataway: IEEE, 2011: 63-70.
21 Wu J B, Yu L F, Zhang Q, et al. Multityped community discovery in time-evolving heterogeneous information networks based on tensor decomposition[J]. Complexity, 2018, 2018(1): 9653404.
22 Jensen S, Liu X Z, Yu Y Y, et al. Generation of topic evolution trees from heterogeneous bibliographic networks[J]. Journal of Informetrics, 2016, 10(2): 606-621.
23 Lee C Y, Kim J, Kwon O, et al. Stochastic technology life cycle analysis using multiple patent indicators[J]. Technological Forecasting and Social Change, 2016, 106: 53-64.
24 张维冲, 王芳, 赵洪. 多源信息融合用于新兴技术发展趋势识别——以区块链为例[J]. 情报学报, 2019, 38(11): 1166-1176.
25 马捷, 李洪晨, 栾泽权. 生命周期视阈下国内应急管理进展研究[J]. 情报理论与实践, 2022, 45(11): 195-204.
26 逯万辉, 谭宗颖. 基于知识基因游离与重组的领域主题演化研究[J]. 情报理论与实践, 2019, 42(2): 101-107.
27 Traag V A, Waltman L, van Eck N J. From Louvain to Leiden: Guaranteeing well-connected communities[J]. Scientific Reports, 2019, 9: Article No.5233.
28 Bródka P, Saganowski S, Kazienko P. GED: the method for group evolution discovery in social networks[J]. Social Network Analysis and Mining, 2013, 3(1): 1-14.
29 Tajeuna E G, Bouguessa M, Wang S R. Tracking the evolution of community structures in time-evolving social networks[C]// Proceedings of 2015 IEEE International Conference on Data Science and Advanced Analytics. Piscataway: IEEE, 2015: 1-10.
30 杨欣谊, 王伟, 朱恒民. 基于时序共词网络的社交平台话题检测与演化研究[J]. 情报学报, 2023, 42(5): 585-597.
31 Carmi S, Havlin S, Kirkpatrick S, et al. A model of Internet topology using k-shell decomposition[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(27): 11150-11154. |
|
|
|
