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Increasing Quality of Austrian Open Data by Linking Them to Linked Data Sources: Lessons Learned

  • Tomáš KnapEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9989)

Abstract

One of the goals of the ADEQUATe project is to improve the quality of the (tabular) open data being published at two Austrian open data portals by leveraging these tabular data to Linked Data, i.e., (1) classifying columns using Linked Data vocabularies, (2) linking cell values against Linked Data entities, and (3) discovering relations in the data by searching for evidences of such relations among Linked Data sources. Integrating data at Austrian data portals with existing Linked (Open) Data sources allows to, e.g., increase data completeness and reveal discrepancies in the data. In this paper, we describe lessons learned from using TableMiner+, an algorithm for (semi)automatic leveraging of tabular data to Linked Data. In particular, we evaluate TableMiner+’s ability to (1) classify columns of the tabular data and (2) link (disambiguate) cell values against Linked Data entities in Freebase. The lessons learned described in this paper are relevant not only for the goals of the ADEQUATe project, but also for other data publishers and wranglers who need to increase quality of open data by (semi)automatically interlinking them to Linked (Open) Data entities.

Keywords

Open Data Linked Data Data quality Data linking Data integration Entity disambiguation 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Faculty of Mathematics and PhysicsCharles University in PraguePraha 1Czech Republic
  2. 2.Semantic Web CompanyViennaAustria

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