Technology mining for emerging S&T trends and developments: Dynamic term clustering and semantic analysis
In the world of rapidly developing Science and Technology (S&T), with increasing volumes of S&T-related data and greater interdisciplinary and collaborative research, technology mining (TM) helps to acquire intelligence about emerging trends and future S&T developments. The task is becoming crucial not only for high-tech startups and large organizations, but also for venture capitalists and other companies, which make decisions about S&T investments. Governments and Public Research Institutions are also among the main stakeholders and potential users of TM to set up R&D priorities, plans and programs according to the current and future state of S&T development. Term clusters built by TM and bibliometric tools based on co-occurrence of authors’ keywords or terms processed from titles and abstracts of scientific documents combine totally different types of objects: research fields, major problems and challenges, methods, inventions, products, technologies and etc. Specific expertise in the field may allow a researcher to identify key objects of the study. However, objects themselves and their frequency dynamics over the time period alone do not fully indicate S&T developments and emerging trends in the area. In order to improve the process of the identification of emerging S&T trends and developments, the paper focuses on dynamic term clustering and suggests a systemic approach to combine TM, bibliometrics, NLP and semantic analysis as part of the unified analytical framework. The approach proposed utilizes existing clustering methods and tools along with the analysis of term linguistic dependencies in order to study changes of objects over the time along with their semantic meanings.