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Knowledge Extraction and Modeling from Scientific Publications

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Semantics, Analytics, Visualization. Enhancing Scholarly Data (SAVE-SD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9792))

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Abstract

During the last decade the amount of scientific articles available online has substantially grown in parallel with the adoption of the Open Access publishing model. Nowadays researchers, as well as any other interested actor, are often overwhelmed by the enormous and continuously growing amount of publications to consider in order to perform any complete and careful assessment of scientific literature. As a consequence, new methodologies and automated tools to ease the extraction, semantic representation and browsing of information from papers are necessary. We propose a platform to automatically extract, enrich and characterize several structural and semantic aspects of scientific publications, representing them as RDF datasets. We analyze papers by relying on the scientific Text Mining Framework developed in the context of the European Project Dr. Inventor. We evaluate how the Framework supports two core scientific text analysis tasks: rhetorical sentence classification and extractive text summarization. To ease the exploration of the distinct facets of scientific knowledge extracted by our platform, we present a set of tailored Web visualizations. We provide on-line access to both the RDF datasets and the Web visualizations generated by mining the papers of the 2015 ACL-IJCNLP Conference.

This work is (partly) supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the European Project Dr. Inventor (FP7-ICT-2013.8.1 - Grant no: 611383).

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Notes

  1. 1.

    http://www.ncbi.nlm.nih.gov/pubmed.

  2. 2.

    http://www.scopus.com/.

  3. 3.

    http://www.webofknowledge.com/.

  4. 4.

    https://doaj.org/.

  5. 5.

    http://jats.nlm.nih.gov/.

  6. 6.

    http://www.elsevier.com/author-schemas/elsevier-xml-dtds-and-transport-schemas.

  7. 7.

    https://rawgit.com/essepuntato/rash/master/documentation/index.html.

  8. 8.

    http://backingdata.org/dri/library/.

  9. 9.

    http://pdfx.cs.man.ac.uk/.

  10. 10.

    http://www.bibsonomy.org/help/doc/api.html.

  11. 11.

    http://search.crossref.org/help/api.

  12. 12.

    http://freecite.library.brown.edu/welcome.

  13. 13.

    https://code.google.com/p/mate-tools/.

  14. 14.

    http://sempub.taln.upf.edu/dricorpus.

  15. 15.

    http://babelfy.org/.

  16. 16.

    http://babelnet.org/.

  17. 17.

    http://www.taln.upf.edu/pages/summa.upf/.

  18. 18.

    Rouge-2 is a measure which compares n-grams in automatic summaries to n-grams in gold stadard summaries.

  19. 19.

    Download link: http://backingdata.org/dri/viz/.

  20. 20.

    http://www.sparontologies.net/.

  21. 21.

    http://backingdata.org/dri/viz/.

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Authors and Affiliations

  1. Natural Language Processing Group (TALN), Universitat Pompeu Fabra, Barcelona, Spain

    Francesco Ronzano & Horacio Saggion

Authors
  1. Francesco Ronzano
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  2. Horacio Saggion
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Correspondence to Francesco Ronzano .

Editor information

Editors and Affiliations

  1. Oxford e-Research Centre, University of Oxford, Oxford, United Kingdom

    Alejandra González-Beltrán

  2. Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

    Francesco Osborne

  3. Dept of Computer Sci & Engineering, University of Bologna, Bologna, Italy

    Silvio Peroni

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Ronzano, F., Saggion, H. (2016). Knowledge Extraction and Modeling from Scientific Publications. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds) Semantics, Analytics, Visualization. Enhancing Scholarly Data. SAVE-SD 2016. Lecture Notes in Computer Science(), vol 9792. Springer, Cham. https://doi.org/10.1007/978-3-319-53637-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-53637-8_2

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