# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 5 Acquisition of Fleet- and Depot-Specific Maintenance Knowledge
### Accessing RailMain-KG  
Automating maintenance scheduling operations, especially within short-term horizons (1-2 weeks), requires up-to-date information about maintenance interventions' due dates, fleet availability constraints, day restrictions and workforce, and availability of required resources. Although most of this information exists in machine-processable format, currently, the tools and materials required for each maintenance intervention are mainly stored in VMI documents.  
This section describes how to utilise the knowledge within RailMain-KG by executing powerful semantic searches in order to inform resource-aware scheduling strategies. In particular, we illustrate the use of RailMain-KG to acquire the essential requirements of a set of maintenance tasks and generate a digital output that can be directly incorporated into scheduling algorithms. This output is obtained through SPARQL queries on the RailMain-KG instance, which is hosted on a GraphDB [31] platform; furthermore, the results are displayed in a tabular format for the sake of readability. We refer the interested reader to [PERSON] et al. [28] to further explore the capabilities of RailMain-KG and the quality assessment of the extracted information.  
**Resource-aware scheduling:** A typical approach adopted by railway maintenance depots for resource allocation is the recurrent resupply cycle. This approach ensures the availability of spare parts and other materials by restocking them on predefined time intervals. The underlying strategy of this approach is to maintain an excess of resources  
Figure 5: Graph representation of a task for changing the coolant filter in the engine. Note: Data properties are skipped for the sake of readability.  
regardless of their actual demand. Although it is an intelligent solution, this method lacks adaptability, which limits any potential for automation. For instance, changes in depot policies, such as modification of maintenance routines, might result in a shortage or unnecessary surplus of resources. Information within the RailMain-KG can inform short-term scheduling processes to proactively manage resource availability. Indicative examples include: (a) postponing tasks when resources are insufficient; (b) prioritiising maintenance tasks based on their criticality and the available stock levels; (c) proactive inventory management to ensure the timely completion of all maintenance tasks. Resource-aware scheduling significantly enhances the inventory management and planning of preventive maintenance. The 'Rail Value for Money Study' suggests that rolling stock maintenance costs have around 27 influential variables [1]; the most influential costs are 13.8% spare part cost, 11% life cycle cost, and 6.4% preventive maintenance costs. The work proposed in this paper will help reduce spare parts management and preventive maintenance costs.  
Figure 6 presents a query that retrieves the requirements for every maintenance task in a selected Preventive Maintenance Exam. For the sake of simplicity, this example includes four random tasks; the output is formatted as a table that provides a consolidated summary of the necessary materials along with their stock availability (stock quantities are arbitrarily set for demonstration purposes). To enhance readability, only five materials are displayed; quantifiers \"exactly\" and \"at least\" indicate whether the VMI specifies an exact quantity for a material or if it should be used as needed. Finally, the priority column classifies the significance of each material element by highlighting its role in tasks that have an impact on the safe operation of a vehicle.