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

• Published in: Journal of internet services and applications Vol. 12; no. 1
• Main Authors: Verma, Amit Arjun, Iyengar, S.R.S, Setia, Simran, Dubey, Neeru
• Format: Journal Article
• Language: English
• Published: London Springer London 01-12-2021; Sociedade Brasileira de Computação
• Subjects: Collaboration; Computer Applications; Computer Communication Networks; Computer Science; Computer Systems Organization and Communication Networks; Data analysis; Encyclopedias; Format; Information Systems and Communication Service; IT in Business; Knowledge; Libraries; Open collaboration research and practice; Processor Architectures; Repositories; Representations; Standard data; Toolkits; Open-source; Wikipedia; Knowledge building; Stack exchange; Python library
• ISSN: 1867-4828; 1869-0238
• DOI: 10.1186/s13174-021-00142-x

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)