Data are key to the effective and safe maintenance of assets over their life cycle. This data is generated by, and drawn from, various sources such as maintenance management systems, design documentation, original equipment manufacturer manuals, process control systems, third party service providers, risk management assessments and failure investigations, to name just a few. However, due to the heterogeneity of data sources and diversity of data types, unlocking the real value of data and discovering the useful patterns of knowledge embedded in the maintenance data has always presented a major challenge. Ontologies can effectively address this challenge by semantic annotation, integration, consistency checking and organization of data. The aim of this project is to demonstrate automation of transactions involving data exchange about asset performance and maintenance work |