Knowledge evolution offers a road map for understanding knowledge creation, knowledge transfer, and performance in everyday work. Understanding the knowledge evolution of a research field is crucial for researchers, policymakers, and stakeholders. Further, paper keywords are considered efficient knowledge components to depict the knowledge structure of a research field by examining relationships between keywords. However, multiple relationships between keywords provided by papers are rarely used to explore knowledge evolution. Three relationships were applied: a direct co-occurrence relationship, indirect relationship by keyword pair citation, and same author trace, providing temporal and sequential knowledge evolution. The direct co-occurrence relationship is constructed by keyword co-occurrence pair and acts as the temporal structure of knowledge pairs. The indirect relationship is constructed by a keyword pair-based citation relationship, meaning the citation relationship between keyword co-occurrence pairs, acting as the sequential structure of knowledge pairs. Additionally, the same author trace represents an indirect relationship that a keyword pair provided by the same author in a different paper. Thus, knowledge evolution could be mined quantitatively from a different perspective. Therefore, we present an empirical study of the informetrics field with five evolution stages: knowledge generation, growth, obsolescence, transfer, and intergrowth. The results indicate that knowledge evolution is not a continuous trend but alternating growth and obsolescence. During evolution, knowledge pairs stimulate each other’s growth, and some knowledge pairs transfer to others, demonstrating a small step toward knowledge change. According to the indirect keyword relationship paired with the same author trace, creators and followers of knowledge evolution are different.
|Number of pages||31|
|Publication status||Published - 2022 Apr|
Bibliographical noteFunding Information:
This study was partially funded by the National Natural Science Foundation of China (NSFC) Grant No. 72104220. This work was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5B1104865).
© 2022, Akadémiai Kiadó, Budapest, Hungary.
All Science Journal Classification (ASJC) codes
- Social Sciences(all)
- Computer Science Applications
- Library and Information Sciences