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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/.

References

  1. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Adv. Neural Inf. Process. Syst. 26, 2787–2795 (2013)

    Google Scholar 

  2. Cai, L., Yan, B., Mai, G., Janowicz, K., Zhu, R.: TransGCN: coupling transformation assumptions with graph convolutional networks for link prediction. In: Proceedings of the 10th International Conference on Knowledge Capture, pp. 131–138 (2019)

    Google Scholar 

  3. Cohan, A., Feldman, S., Beltagy, I., Downey, D., Weld, D.S.: Specter: document-level representation learning using citation-informed transformers (2020). arXiv preprint arXiv:2004.07180

  4. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1) (2019)

    Google Scholar 

  5. Fan, M., Zhou, Q., Chang, E., Zheng, F.: Transition-based knowledge graph embedding with relational mapping properties. In: Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing, pp. 328–337 (2014)

    Google Scholar 

  6. Gao, S., Hu, Y., Janowicz, K., McKenzie, G.: A spatiotemporal scientometrics framework for exploring the citation impact of publications and scientists. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 204–213 (2013)

    Google Scholar 

  7. Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102(46), 16569–16572 (2005)

    Article  MATH  Google Scholar 

  8. Hood, W.W., Wilson, C.S.: The literature of bibliometrics, scientometrics, and informetrics. Scientometrics 52(2), 291–314 (2001). https://doi.org/10.1023/A:1017919924342

    Article  Google Scholar 

  9. Hu, Y., Janowicz, K., McKenzie, G., Sengupta, K., Hitzler, P.: A linked-data-driven and semantically-enabled journal portal for scientometrics. In: International Semantic Web Conference, pp. 114–129. Springer (2013)

    Google Scholar 

  10. Hu, Y., McKenzie, G., Yang, J.A., Gao, S., Abdalla, A., Janowicz, K.: A linked-data-driven web portal for learning analytics: data enrichment, interactive visualization, and knowledge discovery. In: LAK Workshops (2014)

    Google Scholar 

  11. Santha kumar, R., Kaliyaperumal, K.: A scientometric analysis of mobile technology publications. Scientometrics 105(2), 921–939 (2015). https://doi.org/10.1007/s11192-015-1710-7

    Article  Google Scholar 

  12. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

    Google Scholar 

  13. Mai, G., Janowicz, K., Yan, B.: Combining text embedding and knowledge graph embedding techniques for academic search engines. In: Semdeep/NLIWoD@ ISWC, pp. 77–88 (2018)

    Google Scholar 

  14. Mai, G., Janowicz, K., Yan, B., Zhu, R., Cai, L., Lao, N.: Multi-scale representation learning for spatial feature distributions using grid cells. In: International Conference on Learning Representations (2020)

    Google Scholar 

  15. McKenzie, G., Janowicz, K., Hu, Y., Sengupta, K., Hitzler, P.: Linked scientometrics: designing interactive scientometrics with linked data and semantic web reasoning (2013)

    Google Scholar 

  16. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)

    Google Scholar 

  17. Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of NAACL-HLT, pp. 2227–2237 (2018)

    Google Scholar 

  18. Regalia, B., Janowicz, K., Mai, G.: Phuzzy. link: a SPARQL-powered client-sided extensible semantic web browser. In: VOILA@ ISWC, pp. 34–44 (2017)

    Google Scholar 

  19. Schlichtkrull, M., et al.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38

    Chapter  Google Scholar 

  20. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112–1119. AAAI Press (2014)

    Google Scholar 

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06980-2

  • Online ISBN: 978-3-031-06981-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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