The European railway sector faces challenges in digital interoperability due to fragmented data ecosystems and heterogeneous standards. This Mid-Stage PhD research explores how semantic technologies–leveraging open data, knowledge graphs, and standardized frameworks–can enhance data integration and cross-border railway operations. Using an exploratory, constructive, and empirical approach, the study integrates literature analysis, stakeholder collaboration, and real-world implementations with ÖBB, Austria’s national railway provider. Key investigations include case studies on open data and IoT integration for enhancing Rail Accessibility in Park and Ride, domain-specific knowledge graph development, and data-sharing preferences from Open Data to Data Spaces for cross-border rail passenger travelling. Findings are validated through theoretical analysis, practical deployment, and stakeholder feedback. Preliminary results highlight gaps in open data sharing practices and demonstrate the potential of linked open data frameworks to improve interoperability and enhanced benefits of IoT park and ride with linked schedule integration.