Hierarchical Representation and Deep Learning-Based Method for Automatically Transforming Textual Building Codes into Semantic Computable Requirements

Most of the existing automated compliance checking (ACC) systems are unable to fully automatically convert building-code requirements, especially requirements that have hierarchically complex semantic and syntactic structures, into computer-processable forms. The state-of-the-art rule-based ACC methods that are able to deal with complex requirements are based on information extraction and transformation rules, which are inflexible when applied to different types of regulatory documents. More research is thus needed to develop a flexible method to automatically process and understand requirements to support the downstream tasks in ACC systems, such as information matching and compliance reasoning. To address this need, this paper proposes (1) a new representation of requirements, the requirement hierarchy, and (2) a deep learning-based method to automatically extract semantic relations between words from building-code sentences, which are used to transform the sentences into such hierarchies. The proposed method was evaluated using a corpus of sentences from multiple regulatory documents. It achieved high semantic relation and requirement hierarchy extraction performance.