Abstract:
With the success of crowdsourced 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 standard 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
