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A knowledge graph embeddings based approach for author name disambiguation using literals

Author

Listed:
  • Cristian Santini

    (FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
    University of Bologna
    Karlsruhe Institute of Technology, Institute AIFB)

  • Genet Asefa Gesese

    (FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
    Karlsruhe Institute of Technology, Institute AIFB)

  • Silvio Peroni

    (University of Bologna)

  • Aldo Gangemi

    (University of Bologna)

  • Harald Sack

    (FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
    Karlsruhe Institute of Technology, Institute AIFB)

  • Mehwish Alam

    (FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
    Karlsruhe Institute of Technology, Institute AIFB)

Abstract

Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github ( https://github.com/sntcristian/and-kge ) and Zenodo ( https://doi.org/10.5281/zenodo.6309855 ) respectively.

Suggested Citation

  • Cristian Santini & Genet Asefa Gesese & Silvio Peroni & Aldo Gangemi & Harald Sack & Mehwish Alam, 2022. "A knowledge graph embeddings based approach for author name disambiguation using literals," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4887-4912, August.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:8:d:10.1007_s11192-022-04426-2
    DOI: 10.1007/s11192-022-04426-2
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    References listed on IDEAS

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    1. Humaira Waqas & Muhammad Abdul Qadir, 2021. "Multilayer heuristics based clustering framework (MHCF) for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7637-7678, September.
    2. Yu Liu & Weijia Li & Zhen Huang & Qiang Fang, 2015. "A fast method based on multiple clustering for name disambiguation in bibliographic citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(3), pages 634-644, March.
    3. Ricardo G. Cota & Anderson A. Ferreira & Cristiano Nascimento & Marcos André Gonçalves & Alberto H. F. Laender, 2010. "An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1853-1870, September.
    4. Ricardo G. Cota & Anderson A. Ferreira & Cristiano Nascimento & Marcos André Gonçalves & Alberto H. F. Laender, 2010. "An unsupervised heuristic‐based hierarchical method for name disambiguation in bibliographic citations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(9), pages 1853-1870, September.
    5. KM. Pooja & Samrat Mondal & Joydeep Chandra, 2021. "Exploiting similarities across multiple dimensions for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7525-7560, September.
    6. Pooja KM & Samrat Mondal & Joydeep Chandra, 2020. "A Graph Combination With Edge Pruning‐Based Approach for Author Name Disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(1), pages 69-83, January.
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    1. Andrea Ancona & Roy Cerqueti & Gianluca Vagnani, 2023. "A novel methodology to disambiguate organization names: an application to EU Framework Programmes data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4447-4474, August.

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