Text Mining-Based Patent Analysis for Automated Rule Checking in AEC

19th International Conference on Computing in Civil and Building Engineering (ICCCBE 2022), 2022

引用方式: Zheng, Z., Kang, B.R., Yuan, Q.T., Zhou, Y.C., Lu, X.Z., Lin, J.R.* (2022). Text Mining-Based Patent Analysis for Automated Rule Checking in AEC. 19th International Conference on Computing in Civil and Building Engineering (ICCCBE 2022), xxx-xxx. Cape Town, South Africa. https://doi.org/10.48550/arXiv.2212.05891 cited by count

摘要

自动规则检查(ARC)是智能审图的关键技术之一,对提高建筑业(AEC)工程设计的合规性具有重要意义,得到了越来越多的关注。为发现智能审图研究热点并预测其发展趋势,本研究基于Derwent创新指数数据库(DII)和中国知网(CNKI)数据库中的专利为数据源,引入文本挖掘和社交网络分析方法(包括LDA主题抽取、SNA共现分析等),对专利申请数量(即年度分布分析)、专利内容主题及其相互关系进行了分析。结果表明,中文和英文专利的研究热点和趋势具有显著不同。本研究的贡献主要体现在三个方面:(1)提出了一种结合多种文本挖掘技术(即SNA和LDA)对专利内容进行系统分析挖掘的方法;(2)在专利分析的基础上,综述了智能审图的应用热点和发展趋势;(3)为智能审图的未来发展和创新提供了有益的参考。

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 (i.e., 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 (i.e., 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.

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The authors are grateful for the financial support received from the National Natural Science Foundation of China (No. 51908323, No. 72091512), the National Key R&D Program (No. 2019YFE0112800), and the Tencent Foundation through the XPLORER PRIZE.

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