SPRINT: Semantics for PerfoRmant and scalable INteroperability of multimodal Transport

Abstract

Interoperability is a long-standing challenge slowing down the digitalization of mobility systems and the provision of full mobility-as-a-service offerings. This paper presents early results achieved by the SPRINT project (Semantics for PerfoRmant and scalable INteroperability of multimodal Transport), an integral part of the Shift2Rail IP4 work programme, in meeting the challenge. It introduces the conceptual architecture and describes the demonstrator implementation of the Interoperability Framework (IF), a solution designed to support servitization in the mobility domain in two directions: From the Travel Application perspective, the IF provides semantically-consistent abstractions of distributed ICT resources offered by Service Provider Companies, insulating them from the "mechanics" of operating remotely over networks and across multiple communication protocols and/or data formats; from the Service Provider Companies perspective, the IF allows leveraging their native ICT computing environment and resources as elements of an end-to-end integrated intermodal mobility solution, insulating them from the specifics of the customer front-end applications.

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.…”

SMART: Towards Automated Mapping between Data Specifications

“…The technique has been implemented in a prototype tool, called SMART (SPRINT Mapping & Annotation Recommendation Tool), as part of the SPRINT [9] project, which aims to define an innovative Interoperability Framework [10]. The tool supports users in the creation and review of the mappings, and then in the generation of the corresponding annotations.…”

“…c) Finding n similar terms: The W2V model is applied separately to the six sets of terms to get n similar words for each term, where n is a configuration parameter (a positive integer). We tested various values for n (3,5,10,20) to find a good balance between accuracy and efficiency of the approach, and we finally settled on n = 3. For instance, if x and y are the number of terms from the two specifications (XSD file and ontology), respectively, after obtaining n similar terms using the W2V model, the resulting matrices will have size x•(n+1) (for the XSD file) and y • (n + 1) (for the ontology)-notice that each original term is also included.…”

Preference Mining in the Travel Domain

“…This research is performed within the RIDE2RAIL (R2R) [4] project, in the frame of Innovation Programme 4 (IP4) of the Shift2Rail (S2R) initiative [5]. S2R's goal is to build a collaborative ecosystems through its Interoperability Framework [6] that offers a variety of modules such as data conversion approach [7], automated mapping [8] [9], and ontology management [10]. The ecosystem facilitates the interoperability among all the IP4 services (e.g., Booking, Journey Planning, etc.)…”