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Italian Research Conference on Digital Libraries

IRCDL 2017: Digital Libraries and Archives pp 3–19Cite as

Analysing and Discovering Semantic Relations in Scholarly Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 733))

Abstract

Scholarly publishing has seen an ever increasing interest in Linked Open Data (LOD). However, most of the existing datasets are designed as flat translation of legacy data sources into RDF. Although that is a crucial step to address, a lot of useful information is not expressed in RDF, and humans are still required to infer relevant knowledge by reading and making sense of texts. Examples are the reasons why authors cite other papers, the rhetorical structure of scientific discourse, bibliometric measures, provenance information, and so on. In this paper we introduce the Semantic Lancet Project, whose goal is to make available a LOD which includes the formalisation of some useful knowledge hidden within the textual content of papers. We have developed a toolchain for reengineering and enhancing data extracted from some publisher’s legacy repositories. Finally, we show how these data are immediately useful to help humans to address relevant tasks, such as data browsing, expert finding, related works finding, and identification of data inconsistencies.

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Notes

  1. 1.

    http://www.semanticlancet.eu.

  2. 2.

    http://www.developers.elsevier.com/devcms/content-apis.

  3. 3.

    http://www.elsevier.com/about/policies/content-mining-policies.

  4. 4.

    FRED: http://wit.istc.cnr.it/stlab-tools/fred.

  5. 5.

    vn.role:Theme and vn.role:Agent identify the theme and the agent of an event, respectively.

  6. 6.

    The Scholarlydata dataset is the reference linked dataset of the Semantic Web community about papers, people, organisations and events. It is available at http://www.scholarlydata.org/.

  7. 7.

    An in-text reference pointer is the entity in the body of a citing work that denotes a bibliographic reference in the reference list, e.g. “[3]” and “(Handler et al. 2012)”.

  8. 8.

    Available at http://two.eelst.cs.unibo.it/data and http://two.eelst.cs.unibo.it/prov, respectively.

  9. 9.

    http://eelst.cs.unibo.it:8089/.

  10. 10.

    http://www.semanticlancet.eu/citationexplorer.

  11. 11.

    http://www.semanticlancet.eu/abstractfinder.

  12. 12.

    http://www.semanticlancet.eu/reporter.

  13. 13.

    http://data.nature.com.

  14. 14.

    http://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/.

  15. 15.

    http://dblp.l3s.de/dblp++.php.

  16. 16.

    http://ontoware.org/swrc/.

  17. 17.

    http://semantic-web-journal.com:3030/.

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Acknowledgements

This paper was supported by MIUR PRIN 2016 GAUSS Project. We would like to thank Elsevier for granting access to Scopus and ScienceDirect APIs.

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Authors and Affiliations

  1. DASPLab, Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Angelo Di Iorio, Silvio Peroni, Francesco Poggi, Fabio Vitali & Paolo Ciancarini

  2. STLab, Institute of Cognitive Science and Technologies, National Research Council, Rome, Italy

    Andrea Giovanni Nuzzolese

Authors
  1. Angelo Di Iorio
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  2. Andrea Giovanni Nuzzolese
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  3. Silvio Peroni
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  4. Francesco Poggi
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  5. Fabio Vitali
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  6. Paolo Ciancarini
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Corresponding author

Correspondence to Francesco Poggi .

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Editors and Affiliations

  1. University of Modena and Reggio Emilia, Modena, Italy

    Costantino Grana

  2. University of Modena and Reggio Emilia, Modena, Italy

    Lorenzo Baraldi

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Iorio, A.D., Nuzzolese, A.G., Peroni, S., Poggi, F., Vitali, F., Ciancarini, P. (2017). Analysing and Discovering Semantic Relations in Scholarly Data. In: Grana, C., Baraldi, L. (eds) Digital Libraries and Archives. IRCDL 2017. Communications in Computer and Information Science, vol 733. Springer, Cham. https://doi.org/10.1007/978-3-319-68130-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-68130-6_1

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