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Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata Using LLMs

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Knowledge Engineering and Knowledge Management (EKAW 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15370))

Abstract

Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seems unleashed, as automated means for automatic population of said ontologies are lacking, and respective initiatives from the Semantic Web community are not necessarily connected: we propose to make scholarly data more sustainably accessible by leveraging Wikidata’s infrastructure and automating its population in a sustainable manner through LLMs by tapping into unstructured sources like conference Web sites and proceedings texts as well as already existing structured conference datasets. While an initial analysis shows that Semantic Web conferences are only minimally represented in Wikidata, we argue that our methodology can help to populate, evolve and maintain scholarly data as a community within Wikidata.

Our main contributions include (a) an analysis of ontologies for representing scholarly data to identify gaps and relevant entities/properties in Wikidata, (b) semi-automated extraction – requiring (minimal) manual validation – of conference metadata (e.g., acceptance rates, organizer roles, programme committee members, best paper awards, keynotes, and sponsors) from websites and proceedings texts using LLMs. Finally, we discuss (c) extensions to visualization tools in the Wikidata context for data exploration of the generated scholarly data. Our study focuses on data from 105 Semantic Web-related conferences and extends/adds more than 6000 entities in Wikidata. It is important to note that the method can be more generally applicable beyond Semantic Web-related conferences for enhancing Wikidata’s utility as a comprehensive scholarly resource. Source Repository: https://github.com/scholarly-wikidata/

 DOI: https://doi.org/10.5281/zenodo.10989709

 License: Creative Commons CC0 (Data), MIT (Code).

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Notes

  1. 1.

    https://www.ieee.org/about/at-a-glance.html.

  2. 2.

    https://www.acm.org/conferences/about-conferences.

  3. 3.

    https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/.

  4. 4.

    https://openalex.org/.

  5. 5.

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

  6. 6.

    https://www.wikidata.org/wiki/Q119153957.

  7. 7.

    https://bit.ly/3Vs6XNc.

  8. 8.

    https://orkg.org/organizations/Event.

  9. 9.

    https://scholia.toolforge.org/.

  10. 10.

    https://synia.toolforge.org.

  11. 11.

    https://openresearch.org.

  12. 12.

    https://orkg.org/organizations/Event.

  13. 13.

    https://meta.wikimedia.org/wiki/WikiCite.

  14. 14.

    https://www.openresearch.org/wiki/ISWC.

  15. 15.

    https://github.com/lixin4ever/Conference-Acceptance-Rate.

  16. 16.

    https://www.langchain.com/langchain.

  17. 17.

    https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker/.

  18. 18.

    link provided at the end of the paper.

  19. 19.

    https://blog.dblp.org/2022/03/02/dblp-in-rdf/.

  20. 20.

    http://www.scholarlydata.org/sparql/.

  21. 21.

    https://github.com/scholarly-wikidata/scholarly-wikidata/wiki/Mapping-scholarly-data-ontologies-to-Wikidata.

  22. 22.

    https://www.wikidata.org/wiki/Wikidata:Property_proposal/number_of_submissions.

  23. 23.

    https://www.wikidata.org/wiki/Wikidata:Property_proposal/number_of_accepted_contributions.

  24. 24.

    https://w.wiki/9mWB.

  25. 25.

    https://w.wiki/9nnJ.

  26. 26.

    https://github.com/scholarly-wikidata/scholarly-wikidata/wiki/Scholary-Wikidata-Query-Examples.

  27. 27.

    https://github.com/scholarly-wikidata/scholarly-wikidata/blob/fa6bbdc78f69df81ae12b45d8537e1977eee8aa6/docs/EKAW_2024_Paper_Appendix.pdf.

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Acknowledgements

This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/COE12].

Author information

Authors and Affiliations

  1. IBM Research, New York, USA

    Nandana Mihindukulasooriya

  2. Sharda University, Greater Noida, India and TIB Hanover, Hanover, Germany

    Sanju Tiwari

  3. Vienna University for Economics and Business, Vienna, Austria

    Daniil Dobriy & Axel Polleres

  4. DTU Compute, Technical University of Denmark, Lyngby, Denmark

    Finn Årup Nielsen

  5. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Tek Raj Chhetri

  6. Center for Artificial Intelligence (AI) Research Nepal, Sundarharaincha-09, Nepal

    Tek Raj Chhetri

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  1. Nandana Mihindukulasooriya
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  2. Sanju Tiwari
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  3. Daniil Dobriy
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  4. Finn Årup Nielsen
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Corresponding author

Correspondence to Nandana Mihindukulasooriya .

Editor information

Editors and Affiliations

  1. Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France

    Mehwish Alam

  2. Università degli Studi di Verona, Verona, Italy

    Marco Rospocher

  3. KNAW Humanities Cluster, Amsterdam, The Netherlands

    Marieke van Erp

  4. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  5. FIZ Karlsruhe, Eggenstein-Leopoldshafen, Germany

    Genet Asefa Gesese

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Mihindukulasooriya, N., Tiwari, S., Dobriy, D., Nielsen, F.Å., Chhetri, T.R., Polleres, A. (2025). Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata Using LLMs. In: Alam, M., Rospocher, M., van Erp, M., Hollink, L., Gesese, G.A. (eds) Knowledge Engineering and Knowledge Management. EKAW 2024. Lecture Notes in Computer Science(), vol 15370. Springer, Cham. https://doi.org/10.1007/978-3-031-77792-9_15

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