## 4 Materials and Methods
### Developing a Knowledge Graph Using the Driver Profile Ontology  
The integration of the ontology into a KG involved key steps, including the creation of ten realistic user data profiles and mapping these data into the Resource Description Framework (RDF) format. * _Creating realistic user profiles._ We created ten user profiles using RDFLib version 7.1.1., a Python library for working with RDF, to perform the conversion. First, it extracted the main classes from the ontology, then the subclasses to build a hierarchy of the ontology's entities. The hierarchy showed the main categories and types in the ontology, which was useful for analysis. We then extracted object properties and data properties. Once the classes and properties are defined, we could create realistic instances based on user profiles, vehicle capabilities and adaptation conditions. We started by identifying key classes and properties from the ontology that represented different driver characteristics. We identified different types of drivers and the driving behaviours and modes they use. We included comfort settings that each driver type would prefer. Commercial drivers may prefer a higher seat position and a cooler cabin temperature, while older drivers may adjust their settings based on the driving environment. Furthermore, multimedia preferences were also included, for example, music genres providing variability and realism. In addition, most profiles were cross-platform, so they worked on iOS and Android. Regarding data security and privacy,  
Figure 5: Relationships associated with the emergency pedal icon class for use case nos. 1-2 (Section 4.5). \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**No** & **Phase** & **Description** \\ \hline
1 & **Define purpose and scope of ontology** & Identify which aspects of the DVIs can be standardised or tailored and develop boundaries where appropriate. \\ \hline
2 & **Ontology development** & Develop the ontology by focusing on two main parts: (i) providing a guide or table that reflects the classes, properties and relationships of the ontology, and (ii) using tools such as ChatGPT-4o to assist in drafting ontology concepts. \\ \hline
3 & **Enhancing ontology** & Enrich the ontology by integrating new concepts based on standardised DVI elements, informed by the literature review and industry standards. \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of phases in driver profile ontology development for DVIs. we included features like data encryption and biometric authentication, particularly for commercial drivers. Table 4 summarises the results of this instantiation, detailing the configurations of different driver profiles and showcasing their unique preferences and attributes. * _Mapping information into RDF_: After generating the instances, we mapped the information into the RDF format. This step organised the information into a semantic graph, where each user profile and its characteristics were represented as entities connected by well-defined relationships. This approach allowed the data to be interoperable with other systems and ontologies, facilitating information sharing. Complex queries are supported by SPARQL, enabling deep data analysis. In our project, the user profile dataset was relatively small and well-structured, so there was no need for real-time data flow management. We decided to use RDFlib for the conversion process, which allowed us to manually transform these data into RDF triples. During this process, we programmatically defined resources (using URIs), properties and values to populate the graph. Because the data were already well-organised, we did not need extensive preprocessing before the conversion. Using the generated data (Table 5), we created an RDF graph. Each user received a unique URI, which served to represent their preferences, driving habits, and security features. For every user profile, we included