Abstract
The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.
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Biswas, R., Sofronova, R., Alam, M., Heist, N., Paulheim, H., Sack, H. (2021). Do Judge an Entity by Its Name! Entity Typing Using Language Models. In: Verborgh, R., et al. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science(), vol 12739. Springer, Cham. https://doi.org/10.1007/978-3-030-80418-3_12
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DOI: https://doi.org/10.1007/978-3-030-80418-3_12
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