Research Output
Transforming Points of Single Contact Data into Linked Data
  Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming to facilitate service provision activities for EU citizens and businesses. The current work followed the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of digital assets) as well as the GO-FAIR principles and tried to transform raw data into fair data. The innovative part of this work is the mapping of information residing in various governmental portals (Points of Single Contacts) by transforming information appearing in them in RDF format (i.e., as Linked data), in order to make them easily accessible, exchangeable, interoperable and publishable as linked open data. Mapping was performed using the semantic model of a single portal, i.e., the enriched Greek e-GIF ontology and by retrieving and analyzing raw, i.e., non-FAIR data, by defining the semantic model and by making data linkable. The Data mapping process proved to require a significant manual effort and revealed that data value remains unexplored due to poor data representation. It also highlighted the need for appropriately designing and implementing horizontal actions addressing an important number of recipients in an interoperable way.

  • Type:

    Article

  • Date:

    11 August 2022

  • Publication Status:

    Published

  • Publisher

    MDPI AG

  • DOI:

    10.3390/computers11080122

  • Cross Ref:

    10.3390/computers11080122

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Fragkou, P., & Maglaras, L. (2022). Transforming Points of Single Contact Data into Linked Data. Computers, 11(8), Article 122. https://doi.org/10.3390/computers11080122

Authors

Keywords

Linked (open) data, Semantic interoperability, FAIR principles, Data mapping, Governmental data, SPARQL, Ontologies

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