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

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.