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Challenges of Linking Organizational Information in Open Government Data to Knowledge Graphs

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12387)

Abstract

Open Government Data (OGD) is being published by various public administration organizations around the globe. Within the metadata of OGD data catalogs, the publishing organizations (1) are not uniquely and unambiguously identifiable and, even worse, (2) change over time, by public administration units being merged or restructured. In order to enable fine-grained analyzes or searches on Open Government Data on the level of publishing organizations, linking those from OGD portals to publicly available knowledge graphs (KGs) such as Wikidata and DBpedia seems like an obvious solution. Still, as we show in this position paper, organization linking faces significant challenges, both in terms of available (portal) metadata and KGs in terms of data quality and completeness. We herein specifically highlight five main challenges, namely regarding (1) temporal changes in organizations and in the portal metadata, (2) lack of a base ontology for describing organizational structures and changes in public knowledge graphs, (3) metadata and KG data quality, (4) multilinguality, and (5) disambiguating public sector organizations. Based on available OGD portal metadata from the Open Data Portal Watch, we provide an in-depth analysis of these issues, make suggestions for concrete starting points on how to tackle them along with a call to the community to jointly work on these open challenges.

Keywords

Open data Dataset evolution Entity linking Knowledge graphs Knowledge graph evolution 

Notes

Acknowledgements

The authors thank Vincent Emonet, Paola Espinoza-Arias, and Bilal Koteich who contributed preliminary analyses regarding the challenges addressed in this paper. We also thank the organizers of the International Semantic Web Summer school (ISWS) 2019: the idea for this paper origins in discussions at the school.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany
  2. 2.Information SchoolThe University of SheffieldSheffieldUK
  3. 3.Vienna University of Economics and BusinessViennaAustria
  4. 4.Complexity Science Hub ViennaViennaAustria
  5. 5.L3S Research CenterLeibniz University HannoverHanoverGermany

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