Gilad Ravid

Despite being important, time and context have yet to be formally incorporated into the process of visually representing the temporal and contextual proximity between keywords in a concept map. In response to the context and time challenges, this study improves automated conventional concept mapping by measuring the temporal and contextual distance between pairs of co-occurring concepts. After generating a conventional concept map, it is temporally and contextually augmented in this work by applying an unsupervised temporal trend detection algorithm and a novel measure of contextual proximity. This proposed approach is demonstrated and validated without loss of generality for a spectrum of information technologies, showing that the resulting assessments of temporal and contextual proximity are highly correlated with subjective assessments of experts. The contribution of this work is emphasized and magnified against the current growing attention to big data analytics in general and to big text-data analytics in particular.