Developing Automatic-Labeled Topic Modeling Based on SAO Structure for Technology Analysis - Minyoung Park; Sunhye Kim; Byungun Yoon (PLoS ONE, 2025)
Topic modeling has become essential for identifying emerging technology trends, detecting technological concepts, and forecasting advancements. This study introduces a subject-action-object (SAO) based approach to overcome the limitations of existing auto-labeling methodologies in patent documents. In particular, by utilizing the “Bag of SAO” concept, the study aims to construct topic modeling itself on an SAO basis, thereby clarifying the complex relationships within technology. Traditional auto-labeling methods often lack sufficient quantitative evaluation metrics and overlook the functional significance and hierarchical structure of technologies. To address these challenges, we propose an auto-labeling methodology that combines SAO-based topic modeling and scoring with text summarization and network analysis. The proposed model’s effectiveness was evaluated using the ROUGE score alongside others such as relevance, coverage, and discrimination, showing its ability to capture functional meanings within the technological context. To enhance interpretability, we integrated a hierarchical structure based on CPC subclasses, offering a more comprehensive view of technological development and trends. This approach is expected to improve the accuracy of topic labels while providing deeper semantic insights, contributing to more efficient technology management. This study illustrates how SAO-based auto-labeling methodologies can be applied in the field of technology management, highlighting their potential applications in technology innovation, policy-making, and industry applications. Furthermore, by integrating the SAO structure, this research is anticipated to lay the groundwork for developing more refined methodologies for technology forecasting and diagnosis in future studies. Through this, we hope to gain a clearer understanding of the directions of technological advancement and provide strategic insights for the development of new technologies.