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Multilingual Knowledge Graphs and Low-Resource Languages: A Review

Authors Lucie-Aimée Kaffee , Russa Biswas , C. Maria Keet , Edlira Kalemi Vakaj , Gerard de Melo

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Lucie-Aimée Kaffee
  • Hasso-Plattner Institut, Potsdam, Germany
Russa Biswas
  • Hasso-Plattner Institut, Potsdam, Germany
C. Maria Keet
  • University of Cape Town, South Africa
Edlira Kalemi Vakaj
  • Birmingham City University, UK
Gerard de Melo
  • Hasso-Plattner Institut, Potsdam, Germany
  • University of Potsdam, Germany

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Lucie-Aimée Kaffee, Russa Biswas, C. Maria Keet, Edlira Kalemi Vakaj, and Gerard de Melo. Multilingual Knowledge Graphs and Low-Resource Languages: A Review. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 10:1-10:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.10

Abstract

There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine-readable, multilingual linked data, which can be reused across applications. In this paper, we provide an overview of work in the domain of multilingual KGs with a focus on low-resource languages. We review the current state of multilingual KGs along with the different aspects that are crucial for creating KGs with language coverage in mind. Special consideration is given to challenges particular to low-resource languages in KGs. We further provide an overview of applications that yield multilingual KG information as well as downstream applications reusing such multilingual data. Finally, we explore open problems regarding multilingual KGs with a focus on low-resource languages.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
  • Computing methodologies → Semantic networks
Keywords
  • knowledge graphs
  • multilingual
  • low-resource languages
  • review

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