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Analysing Structured Scholarly Data Embedded in Web Pages

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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

Web pages increasingly embed structured data in the form of microdata, microformats and RDFa. Through efforts such as schema.org, such embedded markup have become prevalent, with current studies estimating an adoption by about 26% of all web pages. Similar to the early adoption of Linked Data principles by publishers, libraries and other providers of bibliographic data, such organisations have been among the early adopters, providing an unprecedented source of structured data about scholarly works. Such data, however, is fundamentally different from traditional Linked Data, by being very sparsely linked and consisting of a large amount of coreferences and redundant statements. So far, the scale and nature of embedded scholarly data on the Web has not been investigated. In this work, we provide a study on embedded scholarly data to answer research questions about the depth, syntactic and semantic characteristics and distribution of extracted data, thereby investigating challenges and opportunities for using embedded data as a structured knowledge graph of scholarly information.

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Notes

  1. 1.

    RDFa W3C recommendation: http://www.w3.org/TR/xhtml-rdfa-primer/.

  2. 2.

    http://www.w3.org/TR/microdata.

  3. 3.

    http://microformats.org.

  4. 4.

    http://schema.org.

  5. 5.

    http://www.webdatacommons.org.

  6. 6.

    http://webdatacommons.org/structureddata/2014-12/stats/stats.html.

  7. 7.

    https://en.wikipedia.org/wiki/DBpedia.

  8. 8.

    https://en.wikipedia.org/wiki/Freebase.

References

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  2. Dietze, S., Taibi, D., dAquin, M.: Facilitating scientometrics in learning analytics and educational data mining the LAK dataset. Seman. Web J. (2015)

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  3. Meusel, R., Petrovski, P., Bizer, C.: The webdatacommons microdata, RDFa and microformat dataset series. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 277–292. Springer, Cham (2014). doi:10.1007/978-3-319-11964-9_18

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  4. Meusel, R., Paulheim, H.: Heuristics for fixing common errors in deployed schema.org microdata. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 152–168. Springer, Cham (2015). doi:10.1007/978-3-319-18818-8_10

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Author information

Authors and Affiliations

  1. L3S Research Center, Leibniz Universität Hannover, Hannover, Germany

    Ujwal Gadiraju, Ran Yu & Stefan Dietze

  2. Indian Institute of Technology, Patna, India

    Pracheta Sahoo & Sriparna Saha

Authors
  1. Pracheta Sahoo
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  2. Ujwal Gadiraju
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  3. Ran Yu
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  4. Sriparna Saha
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  5. Stefan Dietze
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Corresponding author

Correspondence to Stefan Dietze .

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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Sahoo, P., Gadiraju, U., Yu, R., Saha, S., Dietze, S. (2016). Analysing Structured Scholarly Data Embedded in Web Pages. 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_10

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  • DOI: https://doi.org/10.1007/978-3-319-53637-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53636-1

  • Online ISBN: 978-3-319-53637-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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