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From raw publications to Linked Data

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Abstract

The continuous development of the Linked Data Web depends on the advancement of the underlying extraction mechanisms. This is of particular interest for the scientific publishing domain, where currently most of the data sets are being created manually. In this article, we present a Machine Learning pipeline that enables the automatic extraction of heading metadata (i.e., title, authors, etc) from scientific publications. The experimental evaluation shows that our solution handles very well any type of publication format and improves the average extraction performance of the state of the art with around 4%, in addition to showing an increased versatility. Finally, we propose a flexible Linked Data-driven mechanism to be used both for refining and linking the automatically extracted metadata.

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Author notes

  1. Tudor Groza

    Present address: School of ITEE, The University of Queensland, R. 78-709, Level 7, GP South (#78), St. Lucia campus, 4072, Queensland, Australia

Authors and Affiliations

  1. DERI, National University of Ireland, Galway, Ireland

    Tudor Groza, Siegfried Handschuh & Stefan Decker

  2. IDA Business Park, Lower Dangan, Galway, Ireland

    Tudor Groza, Siegfried Handschuh & Stefan Decker

  3. DFKI GmbH, Trippstadter Strasse 122, 67663, Kaiserslautern, Germany

    Gunnar AAstrand Grimnes

Authors
  1. Tudor Groza
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  2. Gunnar AAstrand Grimnes
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  3. Siegfried Handschuh
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  4. Stefan Decker
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Correspondence to Tudor Groza.

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Groza, T., Grimnes, G.A., Handschuh, S. et al. From raw publications to Linked Data. Knowl Inf Syst 34, 1–21 (2013). https://doi.org/10.1007/s10115-011-0473-6

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  • DOI: https://doi.org/10.1007/s10115-011-0473-6

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