A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Building Energy Efficiency Related Knowledge Mining Based on Natural Language Processing
With the advent of information technology, information and knowledge become huge and difficult to deal with. Because of the complex work involved in building products, it is very important to excavate the knowledge related. This paper uses natural language processing and Python programming to realize data preprocessing, Chinese segmentation, and relationship extraction based on co-occurrence principle. Finally, 10 documents from CNKI about building energy conservation are chosen. It is found that building energy conservation is significantly related to the concepts of thermal insulation materials and technology, which proves that natural language processing, can effectively help us to excavate knowledge.
Building Energy Efficiency Related Knowledge Mining Based on Natural Language Processing
With the advent of information technology, information and knowledge become huge and difficult to deal with. Because of the complex work involved in building products, it is very important to excavate the knowledge related. This paper uses natural language processing and Python programming to realize data preprocessing, Chinese segmentation, and relationship extraction based on co-occurrence principle. Finally, 10 documents from CNKI about building energy conservation are chosen. It is found that building energy conservation is significantly related to the concepts of thermal insulation materials and technology, which proves that natural language processing, can effectively help us to excavate knowledge.
Building Energy Efficiency Related Knowledge Mining Based on Natural Language Processing
Ma, Dingyuan (author) / Zhang, Bingqing (author) / Li, Xiaodong (author)
International Conference on Construction and Real Estate Management 2020 ; 2020 ; Stockholm, Sweden (Conference Held Virtually)
ICCREM 2020 ; 231-237
2020-10-14
Conference paper
Electronic Resource
English
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