Turning Transport Data to Comply with EU Standards While Enabling a Multimodal Transport Knowledge Graph

Abstract

Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption. This means that datasets are natively expressed in other formats and require a data translation process for full compliance. This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a format compliant with EU standards by means of Semantic Web technologies. Our solution addresses the challenge and also contributes to build a multimodal transport Knowledge Graph of interlinked and interoperable information that enables intelligent querying and exploration, as well as facilitates the design of added-value services.

Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

“…In recent years, there has been an increasing interest in generating knowledge graphs from data with different velocities than static data, such as data streams. Several approaches were introduced, for example: Triple-Wave [18], RDF-Gen [20], SPARQL-Generate [15], and Chimera [21]. However, these approaches do not declaratively describe how different data velocities must be handled during the knowledge graph generation.…”

“…CARML 3 also access streams by extending RML with its own extension, a single access description for streams (carml:Stream) but only describes the name of the stream to use. Recently, a data transformation framework Chimera was proposed [21] which allows to uplift data into a knowledge graph using RML and lower this knowledge graph later on in various data formats through Apache Velocity templates and SPARQL queries [21]. Chimera leverages Apache Camel's Routes [21] for constructing its data processing pipelines.…”

“…Recently, a data transformation framework Chimera was proposed [21] which allows to uplift data into a knowledge graph using RML and lower this knowledge graph later on in various data formats through Apache Velocity templates and SPARQL queries [21]. Chimera leverages Apache Camel's Routes [21] for constructing its data processing pipelines. Because of this, Chimera can have multiple input and output channels and access data sources included in the Apache Camel framework, such as Web APIs or streams.…”

SMART: Towards Automated Mapping between Data Specifications

“…This heterogeneity of data representations significantly hinders the interoperability of the systems to be integrated, and it can be mitigated through the adoption of suitable conversion mechanisms between data specifications. Scrocca et al [6] developed a promising data conversion approach, following the schema described in [7] and shown in Figure 1.…”

“…In this schema, a reference ontology acts as "pivot" between data specifications A and B, whereby specification A DOI reference number: 10.18293/SEKE2021-161 is "lifted" to the ontology (i.e., the concepts in specification A are mapped to those in the ontology), and then the latter is "lowered" to specification B. The approach has proven to be effective [6] and, although it originated from projects focusing on the transportation domain and has been tested using transportation data, it is general and can be applied to any other domain where a reference ontology is available.…”