Text Mining-Based Patent Analysis for Automated Rule Checking in AEC
Abstract
Automated rule checking (ARC), which is expected to promote the efficiency of the compliance checking process in the architecture, engineering, and construction (AEC) industry, is gaining increasing attention. Throwing light on the ARC application hotspots and forecasting its trends are useful to the related research and drive innovations. Therefore, this study takes the patents from the database of the Derwent Innovations Index database (DII) and China national knowledge infrastructure (CNKI) as data sources and then carried out a three-step analysis including (1) quantitative characteristics (ie, annual distribution analysis) of patents,(2) identification of ARC topics using a latent Dirichlet allocation (LDA) and,(3) SNA-based co-occurrence analysis of ARC topics. The results show that the research hotspots and trends of Chinese and English patents are different. The contributions of this study have three aspects:(1) an approach to a comprehensive analysis of patents by integrating multiple text mining methods (ie, SNA and LDA) is introduced;(2) the application hotspots and development trends of ARC are reviewed based on patent analysis; and (3) a signpost for technological development and innovation of ARC is provided.
This work was done in my third year of undergraduate period in Tsinghua University, supervised by Associate Professor Jiarui Lin and Dr. Zhe Zheng.
Borui Kang
PhD Student
My research interests mainly include medical image processing, general computer vision and other deep learning techniques.