An open source library to parse and analyze online collaborative knowledge-building portals

Journal of Internet Services and Applications

Amit Arjun Verma¹  Neeru Dubey¹  Simran Setia¹  S.R.S Iyengar¹ 

[1] Social Computing and Collective Intelligence Lab, Indian Institute of Technology Ropar, Rupnagar, India;

关键词: Knowledge building;    Wikipedia;    Stack exchange;    Open-source;    Python library;   

DOI  :  10.1186/s13174-021-00142-x

来源: Springer

【 摘 要 】

With the success of collaborative knowledge-building portals, such as Wikipedia, Stack Overflow, Quora, and GitHub, a class of researchers is driven towards understanding the dynamics of knowledge building on these portals. Even though collaborative knowledge building portals are known to be better than expert-driven knowledge repositories, limited research has been performed to understand the knowledge building dynamics in the former. This is mainly due to two reasons; first, unavailability of the standard data representation format, second, lack of proper tools and libraries to analyze the knowledge building dynamics.We describe Knowledge Data Analysis and Processing Platform (KDAP), a programming toolkit that is easy to use and provides high-level operations for analysis of knowledge data. We propose Knowledge Markup Language (Knol-ML), a generic representation format for the data of collaborative knowledge building portals. KDAP can process the massive data of crowdsourced portals like Wikipedia and Stack Overflow efficiently. As a part of this toolkit, a data-dump of various collaborative knowledge building portals is published in Knol-ML format. The combination of Knol-ML and the proposed open-source library will help the knowledge building community to perform benchmark analysis.Link of the repository: Verma et al. (2020)Video Tutorial: Verma et al. (2020)Supplementary Material: Verma et al. (2020)

【 授权许可】

CC BY