@inproceedings{hu-etal-2022-transformer,
title = "Transformer-based Entity Typing in Knowledge Graphs",
author = "Hu, Zhiwei and
Gutierrez-Basulto, Victor and
Xiang, Zhiliang and
Li, Ru and
Pan, Jeff",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.402",
doi = "10.18653/v1/2022.emnlp-main.402",
pages = "5988--6001",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Transformer-based Entity Typing in Knowledge Graphs
%A Hu, Zhiwei
%A Gutierrez-Basulto, Victor
%A Xiang, Zhiliang
%A Li, Ru
%A Pan, Jeff
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F hu-etal-2022-transformer
%X 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.
%R 10.18653/v1/2022.emnlp-main.402
%U https://aclanthology.org/2022.emnlp-main.402
%U https://doi.org/10.18653/v1/2022.emnlp-main.402
%P 5988-6001
Markdown (Informal)
[Transformer-based Entity Typing in Knowledge Graphs](https://aclanthology.org/2022.emnlp-main.402) (Hu et al., EMNLP 2022)
ACL
- Zhiwei Hu, Victor Gutierrez-Basulto, Zhiliang Xiang, Ru Li, and Jeff Pan. 2022. Transformer-based Entity Typing in Knowledge Graphs. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5988–6001, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.