Title: Developing a framework to track knowledge convergence in 'big data'

Authors: Santiago Ruiz-Navas; Kumiko Miyazaki

Addresses: Department of Innovation, Graduate School of Innovation Management, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan ' Department of Innovation, Graduate School of Innovation Management, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan

Abstract: Systems to track the early stages of industrial convergence are used to understand technological and scientific developments. Keywords are considered an important indicator to detect knowledge convergence and so far, few reported methods use them. We define two objectives, first to propose a framework to detect knowledge convergence using keywords and second to test this framework by detecting analysing topics converging into 'big data'. We propose a method which uses scientific papers' author keywords as the data source and includes techniques such as word co-occurrence network analysis and established knowledge sources to disambiguate and classify keywords. We analysed scientific publications related to 'big data' for the years 2008-2016 and identified 221 keywords as a proxy of knowledge convergence and grouped them into 11 topics. Among these 11 topics, four were identified as significant adopters of big data knowledge: artificial intelligence, pattern recognition, natural language processing and data science.

Keywords: big data; knowledge convergence; network analysis; word bibliometrics; co-occurrence; Scientometrics; keyword analysis.

DOI: 10.1504/IJTIP.2018.096101

International Journal of Technology Intelligence and Planning, 2018 Vol.12 No.2, pp.121 - 151

Received: 13 Sep 2017
Accepted: 28 Mar 2018
Published online: 12 Nov 2018 *

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