Technology Keyword Analysis Using Graphical Causal Models

Abstract

Technology keyword analysis (TKA) requires a different approach compared to general keyword analysis. While general keyword analysis identifies relationships between keywords, technology keyword analysis must find cause–effect relationships between technology keywords. Because the development of new technologies depends on previously researched and developed technologies, we need to build a causal inference model, in which the previously developed technology is the cause and the newly developed technology is the effect. In this paper, we propose a technology keyword analysis method using casual inference modeling. To understand the causal relationships between technology keywords, we constructed a graphical causal model combining a graph structure with causal inference. To show how the proposed model can be applied to the practical domains, we collected the patent documents related to the digital therapeutics technology from the world patent databases and analyzed them by the graphical causal model. We expect that our research contributes to various aspects of technology management, such as research and development planning.