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Link to original content: https://doi.org/10.1007/978-3-030-11226-4_27
Interactive Text Analysis and Information Extraction | SpringerLink
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Interactive Text Analysis and Information Extraction

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Digital Libraries: Supporting Open Science (IRCDL 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 988))

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Abstract

A lot of work that has been done in the text mining field concerns the extraction of useful information from the full-text of publications. Such information may be links to projects, acknowledgements to communities, citations to software entities or datasets and more. Each category of entities, according to its special characteristics, requires different approaches. Thus it is not possible to build a generic mining platform that could text mine various publications to extract such info. Most of the time, a field expert is needed to supervise the mining procedure, decide the mining rules with the developer, and finally validate the results. This is an iterative procedure that requires a lot of communication among the experts and the developers, and thus is very time-consuming. In this paper, we present an interactive mining platform. Its purpose is to allow the experts to define the mining procedure, set/update the rules, validate the results, while the actual text mining code is produced automatically. This significantly reduces the communication among the developers and the experts and moreover allows the experts to experiment themselves using a user-friendly graphical interface.

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Notes

  1. 1.

    https://www.openaire.eu/.

  2. 2.

    https://www.tornadoweb.org/en/stable/.

  3. 3.

    https://angular.io.

  4. 4.

    https://www.sqlite.org/.

  5. 5.

    MadIS, as well as the vast majority of other UDF systems, expects “functions” to be proper mathematical functions, i.e., to yield the same output for the same input, however, this property is not possible to ascertain automatically since the UDF language (Python) is unconstrained.

  6. 6.

    http://www.numpy.org.

  7. 7.

    https://www.scipy.org.

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Acknowledgements

This work is funded by the European Commission under H2020 projects OpenAIRE-Connect (grant number: 731011) and OpenAIRE-Advance (grant number: 777541).

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Authors and Affiliations

  1. University of Athens, Greece and Athena Research Center, Athens, Greece

    Tasos Giannakopoulos, Yannis Foufoulas, Harry Dimitropoulos & Natalia Manola

Authors
  1. Tasos Giannakopoulos
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  2. Yannis Foufoulas
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  3. Harry Dimitropoulos
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  4. Natalia Manola
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Corresponding author

Correspondence to Yannis Foufoulas .

Editor information

Editors and Affiliations

  1. Italian National Research Council, Pisa, Italy

    Paolo Manghi

  2. Italian National Research Council, Pisa, Italy

    Leonardo Candela

  3. University of Padua, Padua, Italy

    Gianmaria Silvello

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Giannakopoulos, T., Foufoulas, Y., Dimitropoulos, H., Manola, N. (2019). Interactive Text Analysis and Information Extraction. In: Manghi, P., Candela, L., Silvello, G. (eds) Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, vol 988. Springer, Cham. https://doi.org/10.1007/978-3-030-11226-4_27

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  • DOI: https://doi.org/10.1007/978-3-030-11226-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11225-7

  • Online ISBN: 978-3-030-11226-4

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