TechWordNet: Development of semantic relation for technology information analysis using F-term and natural language processing

Abstract

Text analysis on technology has recently been progressing from the level of words to semantic relations between words. However, existing research methods, such as Subject-Action-Object, have focused on specific purposes or analytical techniques. There is an insufficient amount of fundamental study on what types of semantic relations in technical information need to be analysed to provide meaningful information. At the same time, in the field of NLP, the deep learning-based semantic relation model has been establishing as useful for specific tasks. However, there is a limit to applying the NLP model itself for technical analysis because it does not consider the characteristics of the textual information about technology. Therefore, this study proposes a deep learning-based semantic relation model for technology information analysis. First, meaningful types of semantic relations are derived from the text information about technology. By analysing the F-term classification code, which is a multi-dimensional technology hierarchy with descriptions, a technology semantic labelled dataset is constructed. Finally, we develop a classification model that analyses the semantic relations of technology based on the sentence embedding model. This study contributes to the construction of a deep learning model by developing a meaningful type in the analysis of technical information and constructing a technical text dataset with labels. The result of semantic technology relations can also be utilized as a high-quality source for various applications on technology analysis, such as technology tree and technology roadmap. In other words, it has the advantage of being able to provide generalizable technical information that is not dependent on a specific analysis purpose.