Discovering associations among technologies using modified term frequency inverse document frequency
Abstract
In the current era of rapid technological advancement, the generation of innovative and efficient ideas requires a thorough understanding of the interrelationships among various technologies. Owing to the vast volume of data embedded within technological domains, automated methods are essential for uncovering meaningful associations. This paper introduces a methodology based on a generalized Term Frequency-Inverse Document Frequency (TFIDF) model to extract associations among technologies from a diverse corpus of textual sources, including scholarly publications. The resulting associations are represented as a weighted graph, where nodes denote individual technologies and edge weights reflect the strength of their co-occurrence. This structure, termed the “association tech-graph,” serves as a valuable tool for analyzing trends and guiding innovation within industrial sectors. By adjusting model parameters, multiple graph variants can be generated, allowing deeper analytical insights. The findings suggest that combinations such as Aerial Robotics with Advanced Driver Assistance enhance autonomy, while Actuators with Adjustable Hoisting Machines improve operational efficiency in heavy-duty systems.
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