Integrated and efficient mobility requires data sharing among the involved stakeholders. In this direction, regulators and transport authorities have been defining policies to foster mobility data’s digitalisation and online publication. However, creating several heterogeneous data portals for mobility data resulted in a fragmented ecosystem that challenges data accessibility. In this context, metadata is a key enabler to foster the findability and reusability of relevant datasets, but their interoperability across different data portals should be ensured. Moreover, each domain presents specificities on the relevant information that should be encoded through metadata. To solve these issues within the mobility domain, we present mobilityDCAT-AP, a reference metadata specification for mobility data portals defined by putting together domain experts and the Semantic Web community. We report on the work done to develop the metadata model behind mobilityDCAT-AP and the best practices followed in its implementation and publication. Finally, we describe the available educational resources and the activities performed to ensure broader adoption of mobilityDCAT-AP across mobility data portals. We present success stories from early adopters and discuss the challenges they encountered in implementing a metadata specification based on Semantic Web technologies. Resource type: Metadata Specification License: Creative Commons Attribution 4.0 License PID: https://w3id.org/mobilitydcat-ap Repository: https://github.com/mobilityDCAT-AP

With digital transformation, industrial companies today are facing the challenges to change and innovate their business, by leveraging digital technologies and tools to support their processes and their operations. One of their main challenges is the management of the company knowledge, especially when tacit and owned by industry workers. In this paper, we illustrate how knowledge graphs can be the turning point to allow industry workers digitize and exploit the knowledge about the “what”, the “how” and the “why” of their everyday activities.In particular, we focus on the “how” by illustrating the challenges related to procedural knowledge management, i.e., the knowledge about processes and workflows that employees need to follow, and comply with, to correctly execute their tasks, in order to improve efficiency and effectiveness, to reduce risks and human errors and to optimize operations. We also explain the relationship in this context between knowledge graphs and sub-symbolic AI approaches.