Abstract
The entity type information in Knowledge Graphs (KGs) of different languages plays an important role in a wide range of Natural Language Processing applications. However, the entity types in KGs are often incomplete. Multilingual entity typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, multilingual neural language models are exploited to predict the type of an entity from only the name of the entity. The model has been successfully evaluated on multilingual datasets extracted from different language chapters in DBpedia namely German, French, Spanish, and Dutch.
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Biswas, R., Chen, Y., Paulheim, H., Sack, H., Alam, M. (2022). It’s All in the Name: Entity Typing Using Multilingual Language Models. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_7
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DOI: https://doi.org/10.1007/978-3-031-11609-4_7
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