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LD Connect: A Linked Data Portal for IOS Press Scientometrics

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The Semantic Web (ESWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13261))

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Abstract

In this work, we describe a Linked Data portal, LD Connect, which operates on all bibliographic data produced by IOS Press over the past thirty-five years, including more than a hundred thousand papers, authors, affiliations, keywords, and so forth. However, LD Connect is more than just an RDF-based metadata set of bibliographic records. For example, all affiliations are georeferenced, and co-reference resolution has been performed on organizations and contributors including both authors and editors. The resulting knowledge graph serves as a public dataset, web portal, and query endpoint, and it acts as a data backbone for IOS Press and various bibliographic analytics. In addition to the metadata, LD Connect is also the first portal of its kind that publicly shares document embeddings computed from the full text of all papers and knowledge graph embeddings based on the graph structure, thereby enabling semantic search and automated IOS Press scientometrics. These scientometrics run directly on top of the graph and combine it with the learned embeddings to automatically generate data visualizations, such as author and paper similarity over all journals. By making the involved ontologies, embeddings, and scientometrics all publicly available, we aim to share LD Connect services with not only the Semantic Web community but also the broader public to facilitate research and applications based on this large-scale academic knowledge graph. Particularly, the presented scientometric system generalizes beyond IOS Press data and can be deployed on top of other bibliographic datasets as well.

Z. Liu and M. Shi—Both authors contributed equally to this work.

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Notes

  1. 1.

    https://www.semanticscholar.org/product/api.

  2. 2.

    https://github.com/allenai/paper-embedding-public-apis#specter.

  3. 3.

    https://www.aminer.cn/knowledge_graph.

  4. 4.

    https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema.

  5. 5.

    https://github.com/allenai/specter.

  6. 6.

    https://github.com/stko-lab/LD-Connect.

  7. 7.

    http://bibliontology.com/specification.html.

  8. 8.

    https://www.ogc.org/standards/geosparql.

  9. 9.

    http://ld.iospress.nl/sparql.

  10. 10.

    http://stko-roy.geog.ucsb.edu:7200/iospress_scientometrics.

  11. 11.

    https://d3js.org.

  12. 12.

    https://leafletjs.com.

  13. 13.

    http://ld.iospress.nl/scientometrics/.

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Authors and Affiliations

  1. STKO Lab, UC Santa Barbara, Santa Barbara, CA, USA

    Zilong Liu, Meilin Shi, Krzysztof Janowicz, Blake Regalia, Gengchen Mai & Rui Zhu

  2. IOS Press, Amsterdam, The Netherlands

    Stephanie Delbecque

  3. Department of Computer Science, Stanford University, Stanford, CA, USA

    Gengchen Mai

  4. Data Semantics Lab, Kansas State University, Manhattan, KS, USA

    Pascal Hitzler

Authors
  1. Zilong Liu
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  2. Meilin Shi
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  3. Krzysztof Janowicz
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  4. Blake Regalia
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  5. Stephanie Delbecque
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  6. Gengchen Mai
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  7. Rui Zhu
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  8. Pascal Hitzler
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Corresponding author

Correspondence to Zilong Liu .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Liu, Z. et al. (2022). LD Connect: A Linked Data Portal for IOS Press Scientometrics. In: Groth, P., et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-06981-9_19

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