TAIPAN: Automatic Property Mapping for Tabular Data

  • Ivan Ermilov
  • Axel-Cyrille Ngonga Ngomo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)


The Web encompasses a significant amount of knowledge hidden in entity-attributes tables. Bridging the gap between these tables and the Web of Data thus has the potential to facilitate a large number of applications, including the augmentation of knowledge bases from tables, the search for related tables and the completion of tables using knowledge bases. Computing such bridges is impeded by the poor accuracy of automatic property mapping, the lack of approaches for the discovery of subject columns and the mere size of table corpora. We propose Taipan, a novel approach for recovering the semantics of tables. Our approach begins by identifying subject columns using a combination of structural and semantic features. It then maps binary relations inside a table to predicates from a given knowledge base. Therewith, our solution supports both the tasks of table expansion and knowledge base augmentation. We evaluate our approach on a table dataset generated from real RDF data and a manually curated version of the T2D gold standard. Our results suggest that we outperform the state of the art by up to 85 % F-measure.


Web tables Knowledge base augmentation Table expansion 



This work has been supported by Eurostars projects DIESEL (project no. 01QE1512C), the BMWI Project GEISER (project no. 01MD16014) as well as the European Union’s H2020 research and innovation action HOBBIT (GA no. 688227).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of Leipzig, Institute of Computer ScienceLeipzigGermany
  2. 2.AKSW Research GroupLeipzigGermany

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