Discovering multimodal routes using connection patterns

Abstract

Multimodality requires integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging with being isolated from previously existing networks. This lead them to publish their data sources to the web - according to Linked Data Principles - in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The problem is that current link discovery solutions offer integration in terms of equivalence detection. However, in our context more complex relations with more sophisticated patterns are required. In this paper we present an approach that enables a flexible and customized way to generate connections between RDF datasets. Our approach enables users to create and customize connections, linking functions and linking rules without being restricted to what existing tools provide. These connections offer new information that optimize planned trips in route discovery for example and thus link one transportation mode to another.