Leveraging Semantic Technologies for Digital Interoperability in the European Railway Domain

Abstract

The European Union Agency for Railways is an European authority, tasked with the provision of a legal and technical framework to support harmonized and safe cross-border railway operations throughout the EU. So far, the agency relied on traditional application-centric approaches to support the data exchange among multiple actors interacting within the railway domain. This lead however, to isolated digital environments that consequently added barriers to digital interoperability while increasing the cost of maintenance and innovation. In this work, we show how Semantic Web technologies are leveraged to create a semantic layer for data integration across the base registries maintained by the agency. We validate the usefulness of this approach by supporting route compatibility checks, a highly demanded use case in this domain, which was not available over the agency's registries before. Our contributions include (i) an official ontology for the railway infrastructure and authorized vehicle types, including 28 reference datasets; (ii) a reusable Knowledge Graph describing the European railway infrastructure; (iii) a cost-efficient system architecture that enables high-flexibility for use case development; and (iv) an open source and RDF native Web application to support route compatibility checks. This work demonstrates how data-centric system design, powered by Semantic Web technologies and Linked Data principles, provides a framework to achieve data interoperability and unlock new and innovative use cases and applications. Based on the results obtained during this work, ERA officially decided to make Semantic Web and Linked Data-based approaches, the default setting for any future development of the data, registers and specifications under the agency's remit for data exchange mandated by the EU legal framework. The next steps, which are already underway, include further developing and bringing these solutions to a production-ready state.

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

“…Consequently, these declarative specifications of KGs as RML DISs promote modularity, supporting maintenance, testing, debugging, and enabling collaborative definition and reusability. RML is currently being used by several companies and public organizations [19,20,50,60] to create their KGs, also including the Google Enterprise Knowledge Graph. 9 Fig.…”

“…-Open Cities 49 is a Spanish national project on creating common and shared vocabularies for Spanish cities; SDM-RDFizer executes the RML mapping rule for integrating geographical data for Spanish cities, hence ensuring the interoperability between open data in Spain throughout the created KGs. -Virtual Platform for the H2020 European Joint Programme on Rare Disease, 50 and SDM-RDFizer merges data collected from the consortium partners, thus, satisfying the requirements in Section 2.3.…”

Editorial of transport data on the web

“…However, such challenges have also been posed in other domains such as the Internet of Things [18], agriculture [11], building data management [17], biology [7] or open data [2], which have found their solutions using semantic web technologies. However, despite several research contributions [6,14,19,23,25], public-funded projects 1,2 or academic-industry events, 3,4 we have not yet seen a wide adoption of semantic technologies in the transport domain.…”

Systematic Construction of Knowledge Graphs for Research-Performing Organizations

“…Although it provides a general framework for creating virtual knowledge graphs from relational databases, the mapping rules are quite simple (around 53 rules), and details on how they have been generated or their sustainability are not described in depth. Another successful use case in which mapping rules were used to construct knowledge graphs is reported in [23], where the European Union Agency for Railways (ERA) uses semantic web technologies to describe the European railway infrastructure. The knowledge graph is created from 28 heterogeneous data sources (a relational database and CSV files), and the mappings are declared using (YARR)RML [24].…”