A semi-supervised framework for generating multi-dimensional taxonomies from asset maintenance documents

The operation and maintenance of buildings generate large volumes of unstructured textual data, such as inspection reports and service requests. These records contain valuable insights that can support fault detection, cost tracking, and resource planning. However, existing classification approaches often rely on static, expert-defined labels that fail to reflect the complexity of real-world maintenance operations. This paper introduces a hybrid framework that combines sentence embedding, clustering, topic modeling, and network modularization to uncover recurring patterns in maintenance text. The extracted patterns are then reviewed and refined by facility management experts to develop a multi-dimensional taxonomy model tailored to operational needs. The methodology is applied to a case study involving over 30,000 work orders. The results demonstrate how the proposed system captures fine-grained details such as system type, failure mode, and required trade expertise. A proof-of-concept software tool, developed in collaboration with facility managers, showcases the practical value of the taxonomy in enabling data-driven decision-making, such as identifying cost drivers and recurring issues. Additionally, the resulting taxonomy models serve as effective prompts for zero-shot text classification, enabling large language models to classify new maintenance records without requiring retraining or labeled data. This approach provides a scalable and adaptable foundation for text classification systems in asset management. • A multi-dimensional classification system enhances maintenance record analysis. • Traditional classification models lack the detail needed for business decisions. • A data-driven approach for constructing customized taxonomy models is proposed. • A business intelligence software is developed and published for open access. • Taxonomies can be used for zero-shot learning.