IDEAS home Printed from https://ideas.repec.org/r/eee/infome/v11y2017i4p1175-1189.html
   My bibliography  Save this item

Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Jian Xu & Yi Bu & Ying Ding & Sinan Yang & Hongli Zhang & Chen Yu & Lin Sun, 2018. "Understanding the formation of interdisciplinary research from the perspective of keyword evolution: a case study on joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 973-995, November.
  2. David Doloreux & Jose Gaviria de la Puerta & Iker Pastor-López & Igone Porto Gómez & Borja Sanz & Jon Mikel Zabala-Iturriagagoitia, 2019. "Territorial innovation models: to be or not to be, that’s the question," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1163-1191, September.
  3. WATANABE Ichiro & SHIMIZU Hiroshi, 2024. "Mainstream Formation and Competitive Dynamics in the Computer Graphics Industry: Topic modeling analysis of US patents," Discussion papers 24018, Research Institute of Economy, Trade and Industry (RIETI).
  4. Mario Coccia & Saeed Roshani, 2024. "Evolution of topics and trends in emerging research fields: multiple analyses with entity linking, Mann–Kendall test and burst methods in cloud computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5347-5371, September.
  5. Chen, Hongshu & Jin, Qianqian & Wang, Ximeng & Xiong, Fei, 2022. "Profiling academic-industrial collaborations in bibliometric-enhanced topic networks: A case study on digitalization research," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
  6. Yating Li & Ye Chen & Qiyu Wang, 2021. "Evolution and diffusion of information literacy topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4195-4224, May.
  7. Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
  8. Huichen Gao & Shijuan Wang, 2022. "The Intellectual Structure of Research on Rural-to-Urban Migrants: A Bibliometric Analysis," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
  9. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
  10. Baitong Chen & Ying Ding & Feicheng Ma, 2018. "Semantic word shifts in a scientific domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 211-226, October.
  11. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
  12. Huixin Wang & Jing Xie & Shixian Luo & Duy Thong Ta & Qian Wang & Jiao Zhang & Daer Su & Katsunori Furuya, 2023. "Exploring the Interplay between Landscape Planning and Human Well-Being: A Scientometric Review," Land, MDPI, vol. 12(7), pages 1-24, June.
  13. Jian Xu & Ying Ding & Yi Bu & Shuqing Deng & Chen Yu & Yimin Zou & Andrew Madden, 2019. "Interdisciplinary scholarly communication: an exploratory study for the field of joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1597-1619, June.
  14. Lukun Zheng & Yuhang Jiang, 2022. "Combining dissimilarity measures for quantifying changes in research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3751-3765, July.
  15. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
  16. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
  17. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
  18. Zhentao Liang & Jin Mao & Kun Lu & Gang Li, 2021. "Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9519-9542, December.
  19. Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
  20. David Chavalarias & Quentin Lobbé & Alexandre Delanoë, 2022. "Draw me Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 545-575, January.
  21. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
  22. Mauricio Marrone, 2020. "Application of entity linking to identify research fronts and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 357-379, January.
  23. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
  24. Qian, Yue & Liu, Yu & Sheng, Quan Z., 2020. "Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence," Journal of Informetrics, Elsevier, vol. 14(3).
  25. Jinqing Yang & Zhifeng Liu & Yong Huang, 2024. "From informal to formal: scientific knowledge role transition prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4909-4935, August.
  26. Hengmin Zhu & Li Qian & Wang Qin & Jing Wei & Chao Shen, 2022. "Evolution analysis of online topics based on ‘word-topic’ coupling network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3767-3792, July.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.