@inproceedings{portisch_challenges_2020, address = {Cham}, series = {Lecture {Notes} in {Computer} {Science}}, title = {Challenges of {Linking} {Organizational} {Information} in {Open} {Government} {Data} to {Knowledge} {Graphs}}, isbn = {978-3-030-61244-3}, doi = {10/gh38b7}, 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.}, language = {en}, booktitle = {Knowledge {Engineering} and {Knowledge} {Management}}, publisher = {Springer International Publishing}, author = {Portisch, Jan and Fallatah, Omaima and Neumaier, Sebastian and Jaradeh, Mohamad Yaser and Polleres, Axel}, editor = {Keet, C. Maria and Dumontier, Michel}, year = {2020}, keywords = {Dataset evolution, Depart Informatik und Security, Entity linking, Extern, Forschungsgruppe Data Intelligence, Institut für IT Sicherheitsforschung, Knowledge graph evolution, Knowledge graphs, Open data, Wiss. Beitrag, peer-reviewed}, pages = {271--286}, }