A semantic information alignment method is proposed to align the representations used in building information models (BIMs) to the representations used in energy regulations. Compared to existing alignment efforts, which are either manual or semi-automated, the proposed method aims to automate the alignment process for supporting fully automated energy compliance checking. A first-level simple alignment method is proposed to align single design information instances to single regulatory concepts, in which (1) domain knowledge is used for interpreting the meaning of concepts to recognize candidate instances, and (2) deep learning is used for capturing the semantics behind the words to measure semantic similarity and select the matches. A final complex alignment method is proposed to recognize the instance groups belonging to a regulatory requirement, in which (1) supervised and unsupervised searching algorithms are used to identify the instance pairs, and (2) network modeling is used to group and link the instance pairs to the requirement. The proposed method showed 93.4% recall and 94.7% precision on the testing data.

提出了一种语义信息对齐方法，以将建筑信息模型（BIM）中使用的表示与能源法规中使用的表示对齐。与现有的手动或半自动对准方法相比，所提出的方法旨在使对准过程自动化，以支持全自动能源符合性检查。提出了一种用于将单个设计信息实例与单个监管概念对齐的第一级简单对齐方法，其中（1）领域知识用于解释概念的含义以识别候选实例，（2）深度学习用于捕获单词后面的语义以测量语义相似度并选择匹配项。提出了一种最终的复杂对齐方法来识别属于监管要求的实例组，其中（1）使用有监督和无监督的搜索算法来识别实例对，（2）使用网络建模来对实例进行分组和链接与要求配对。所提出的方法在测试数据上显示出93.4％的查全率和94.7％的准确度。