Asset Information Model (AIM) is a key technology for predictive maintenance in building facilities. AIMs face interoperability issues that can be improved through an Ontology, however, there is a lack of investigation in the use of Ontologies for predictive maintenance in building facilities. The current study proposes the development of an ontology-based AIM to enhance predictive maintenance practices in building facilities. The AIM acts as a centralized repository of information about the building facility's components, systems, and operations, although it faces data interoperability hindrances. To address data interoperability challenges, an Ontology-based AIM for a digital twin platform is proposed to facilitate effective information management. This study explores the processes, data integration, and potential benefits of the ontology-based AIM through a scoping review, ontology development, and interviews with experts. The findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector. The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital twin platform. The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the Operation and Maintenance (O&M)process, optimizing predictive maintenance practices, and ultimately enhancing energy efficiency and sustainability in the building industry.