Semantic relation evaluation of data science articles using network of mention

Data science continues to provide state-of-the-art disruptive breakthroughs to the cybersecurity research business in order to preserve cyberspace for seamless possibilities. While various systematic literature reviews attempt to document these innovative techniques and approaches in order to inspire future research directions, semantic relation extraction's promises have not been utilized in cybersecurity data science reviews, narrowing existing reviews to the bother-line of research questions. However, in this study, a network of mention approach is used to evaluate semantic relationships in data science studies for cybersecurity using the degree of centrality as a metric. To generate edges linking research nodes with existing unanimity in their research method, a 6-number semantic relation threshold is used. The experimental outcome returned a book publishing as research after analyzing 38 original studies.