Towards digital-twin-enabled facility management: the natural language processing model for managing facilities in buildings
Linxuan Wang, Nanting Chen
Abstract
Facility management is an essential part of the building which encompasses multiple service areas and processes to ensuring optimum functionality of the facility. Digital twin (DT) has been increasingly introduced to the FM domain to facilitate FM tasks such as decision-making. Despite the advancements in DT-enabled FM, there still exists a problem in the disconnections between DT academics and FM practitioners. The emergence of knowledge management can act as a bridge between academics and FM practitioners. To mine knowledge in the DT-enabled FM industry for a better communication between different stakeholders and timely decisions in the case of emergency, this study proposes a natural language processing (NLP)-based approach. A detailed classification of FM-related entities is first presented, which divides the FM into five categories. The NLP model is used to extract entities and knowledge from a large volume of text data. The results indicate that an NLP-based approach can accurately extract FM-related knowledge. The findings of this study not only provide a basis for automatic DT-enabled FM-related knowledge extraction but also support the downstream tasks such as knowledge graph construction and FM-related emergency decision-making.