Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

Abstract

Purpose Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA). Design/methodology/approach The paper presents a temporal trend detection algorithm – vector space model – designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies. Findings The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreement measures and average predictive validity above 85 per cent. Practical implications Using massive amounts of textual documents available on the Web to first generate a concept map and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics. Originality/value This paper proposes a novel knowledge discovery method to improve a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.

The disruption of the international supply chain: Firm resilience and knowledge preparedness to tackle the COVID-19 outbreak

“…In other words, knowledge management confirmed its paramount role for building firm resilience ( Ali et al, 2017 ) through implementing continuous innovative processes based on knowledge acquisition, creation and sharing. Knowledge acquisition capabilities, knowledge generation capabilities and knowledge combination capabilities represent three relevant dimensions of knowledge-based dynamic capabilities ( Del Giudice et al, 2017 ; Mital et al, 2018 ; Santoro et al, 2019 ; Sasson et al, 2017 ; Zheng et al, 2011 ). Taking inspiration from these categories, the results confirmed the impact of knowledge acquisition capabilities and knowledge combination capabilities on knowledge preparedness as a new form of knowledge (not just operational) affecting readiness and the ability to prevent, detect and respond to unpredictable and harsh outbreaks.…”

“…The focus on speed determines a paradigm shift from the concept of the first-mover advantage to that of the fast-mover advantage ( Hawk et al, 2013 ). However, to achieve such superior performance through time-based competition, the supply chain must develop superior knowledge capabilities ( Del Giudice et al, 2017 ; Mital et al, 2018 ; Santoro et al, 2019 ; Sasson et al, 2017 ). Timely response to customers stems from readiness and high coordination within the entire ecosystem ( Cha, 2020 ; Meschi et al, 2017 ; Soto-Acosta et al, 2018 ).…”