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Transformer-based entity typing in knowledge graphs

Hu, Zhiwei, Gutierrez Basulto, Victor ORCID:, Xiang, Zhiliang ORCID:, Li, Ru and Pan, Jeff Z. 2022. Transformer-based entity typing in knowledge graphs. Presented at: Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, 07-11 December 2022. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics., pp. 5988-6001.

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We investigate the knowledge graph entity typing task which aims at inferring plausible entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) approach, effectively encoding the content of neighbours of an entity by means of a transformer mechanism. More precisely, TET is composed of three different mechanisms: a local transformer allowing to infer missing entity types by independently encoding the information provided by each of its neighbours; a global transformer aggregating the information of all neighbours of an entity into a single long sequence to reason about more complex entity types; and a context transformer integrating neighbours content in a differentiated way through information exchange between neighbour pairs, while preserving the graph structure. Furthermore, TET uses information about class membership of types to semantically strengthen the representation of an entity. Experiments on two real-world datasets demonstrate the superior performance of TET compared to the state-of-the-art.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Association for Computational Linguistics.
Date of First Compliant Deposit: 17 November 2022
Last Modified: 24 Jan 2023 16:07

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