Complex networks reveal emergent interdisciplinary knowledge in Wikipedia
| Autor: | Gustavo Ariel Schwartz |
|---|---|
| Přispěvatelé: | Ministerio de Ciencia e Innovación (España), Donostia International Physics Center, Ministerio de Ciencia, Innovación y Universidades (España), NVIDIA Corporation |
| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
Computer science Formalism (philosophy) General Arts and Humanities 05 social sciences Social Sciences General Social Sciences 020206 networking & telecommunications 02 engineering and technology Complex network General Business Management and Accounting Data science Bridge (interpersonal) Knowledge extraction AZ20-999 0202 electrical engineering electronic engineering information engineering History of scholarship and learning. The humanities 0501 psychology and cognitive sciences General Economics Econometrics and Finance General Psychology Scientific activity science 050104 developmental & child psychology Meaning (linguistics) |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname Addi. Archivo Digital para la Docencia y la Investigación Humanities & Social Sciences Communications, Vol 8, Iss 1, Pp 1-6 (2021) Addi: Archivo Digital para la Docencia y la Investigación Universidad del País Vasco |
| Popis: | In the last 2 decades, a great amount of work has been done on data mining and knowledge discovery using complex networks. These works have provided insightful information about the structure and evolution of scientific activity, as well as important biomedical discoveries. However, interdisciplinary knowledge discovery, including disciplines other than science, is more complicated to implement because most of the available knowledge is not indexed. Here, a new method is presented for mining Wikipedia to unveil implicit interdisciplinary knowledge to map and understand how different disciplines (art, science, literature) are related to and interact with each other. Furthermore, the formalism of complex networks allows us to characterise both individual and collective behaviour of the different elements (people, ideas, works) within each discipline and among them. The results obtained agree with well-established interdisciplinary knowledge and show the ability of this method to boost quantitative studies. Note that relevant elements in different disciplines that rarely directly refer to each other may nonetheless have many implicit connections that impart them and their relationship with new meaning. Owing to the large number of available works and to the absence of cross-references among different disciplines, tracking these connections can be challenging. This approach aims to bridge this gap between the large amount of reported knowledge and the limited human capacity to find subtle connections and make sense of them. The author acknowledges the financial support from the Spanish Government ‘Ministerio de Ciencia e Innovación’ (PID2019-104650GB-C21) and from the Donostia International Physics Center (Programa Mestizajes), as well as the support of NVIDIA Corporation with the donation of a Quadro RTX 6000 GPU used for this research. |
| Databáze: | OpenAIRE |
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