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Extracting Information about Research Resources from Scholarly Papers

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13636))

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Abstract

This paper presents a method to extract information about research resource metadata from scholarly papers to expand research resource repositories using an academic knowledge graph. Here, we considered the hypothesis that information extracted from scholarly papers is beneficial to expand research resource repositories from two perspectives: (1) the amount of information in existing metadata, and (2) the number of entries in existing repositories. We constructed an academic knowledge graph, where the nodes and directed edges in the graph correspond to the entities and their relations in scholarly papers, respectively. To verify our hypothesis, we constructed a knowledge graph using 15,721 papers published in international conferences. We then investigated the expandability of a language resource metadata repository using the constructed knowledge graph. The experimental results demonstrated that the constructed knowledge graph could be used to enrich the descriptions of metadata and increase the number of entries in the repository.

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Notes

  1. 1.

    https://citeseerx.ist.psu.edu.

  2. 2.

    https://scholar.google.com.

  3. 3.

    https://www.semanticscholar.org.

  4. 4.

    https://cir.nii.ac.jp.

  5. 5.

    https://schema.org.

  6. 6.

    https://jpcoar.repo.nii.ac.jp.

  7. 7.

    https://github.com/pd3f/dehyphen.

  8. 8.

    In addition, if the entity matched a language name included in the ISO 639–3 language code (https://iso639-3.sil.org), it was excluded.

  9. 9.

    This means that there may be approximately 4,000 LR entities in E.

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Acknowledgments

This research was supported in part by the Grant-in-Aid for Scientific Research (B) (No. 21H03773) of the JSPS.

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Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Ayahito Saji & Shigeki Matsubara

  2. Information and Communications, Nagoya University, Nagoya, Japan

    Shigeki Matsubara

Authors
  1. Ayahito Saji
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  2. Shigeki Matsubara
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Correspondence to Ayahito Saji .

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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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Saji, A., Matsubara, S. (2022). Extracting Information about Research Resources from Scholarly Papers. 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_35

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  • DOI: https://doi.org/10.1007/978-3-031-21756-2_35

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  • Online ISBN: 978-3-031-21756-2

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