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Scholarly Knowledge Extraction from Published Software Packages

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From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries (ICADL 2022)

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Abstract

A plethora of scientific software packages are published in repositories, e.g., Zenodo and figshare. These software packages are crucial for the reproducibility of published research. As an additional route to scholarly knowledge graph construction, we propose an approach for automated extraction of machine actionable (structured) scholarly knowledge from published software packages by static analysis of their (meta)data and contents (in particular scripts in languages such as Python). The approach can be summarized as follows. First, we extract metadata information (software description, programming languages, related references) from software packages by leveraging the Software Metadata Extraction Framework (SOMEF) and the GitHub API. Second, we analyze the extracted metadata to find the research articles associated with the corresponding software repository. Third, for software contained in published packages, we create and analyze the Abstract Syntax Tree (AST) representation to extract information about the procedures performed on data. Fourth, we search the extracted information in the full text of related articles to constrain the extracted information to scholarly knowledge, i.e. information published in the scholarly literature. Finally, we publish the extracted machine actionable scholarly knowledge in the Open Research Knowledge Graph (ORKG).

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Notes

  1. 1.

    https://www.dbpedia.org.

  2. 2.

    https://www.wikidata.org/wiki/Wikidata:Main_Page.

  3. 3.

    https://hi-knowledge.org.

  4. 4.

    https://covid-aqs.fz-juelich.de.

  5. 5.

    https://paperswithcode.org.

  6. 6.

    https://cooperationdatabank.org.

  7. 7.

    https://zenodo.org.

  8. 8.

    https://developers.zenodo.org.

  9. 9.

    https://www.orkg.org/orkg/.

  10. 10.

    https://github.com/gitpython-developers/GitPython.

  11. 11.

    https://zenodo.org/record/5874955.

  12. 12.

    https://docs.python.org/3/library/ast.html.

  13. 13.

    https://api.unpaywall.org/v2/10.1186/s12920-019-0613-5?email=unpaywall_01@example.com.

  14. 14.

    https://orkg.org/paper/R209873.

  15. 15.

    https://orkg.org/content-type/Software/R209880.

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Acknowledgment

This work was co-funded by the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536) and TIB–Leibniz Information Centre for Science and Technology.

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Authors and Affiliations

  1. L3S Research Center, Leibniz University Hannover, 30167, Hannover, Germany

    Muhammad Haris, Markus Stocker & Sören Auer

  2. TIB—Leibniz Information Centre for Science and Technology, Hannover, Germany

    Markus Stocker & Sören Auer

Authors
  1. Muhammad Haris
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Correspondence to Muhammad Haris .

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Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Yuen-Hsien Tseng

  2. Doshisha University, Kyoto, Japan

    Marie Katsurai

  3. VNU University of Engineering and Technology, Hanoi, Vietnam

    Hoa N. Nguyen

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Haris, M., Stocker, M., Auer, S. (2022). Scholarly Knowledge Extraction from Published Software Packages. In: Tseng, YH., Katsurai, M., Nguyen, H.N. (eds) From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries. ICADL 2022. Lecture Notes in Computer Science, vol 13636. Springer, Cham. https://doi.org/10.1007/978-3-031-21756-2_24

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