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    <abstract>Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an entity into its representation. However, they ignore the semantic knowledge provided by the way in which types can be clustered together. In this paper, we propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing MCLET, which effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings. MCLET is composed of three modules: i) Multi-view Generation and Encoder module, which encodes structured information from entity-type, entity-cluster and cluster-type views; ii) Cross-view Contrastive Learning module, which encourages different views to collaboratively improve view-specific representations of entities and types; iii) Entity Typing Prediction module, which integrates multi-head attention and a Mixture-of-Experts strategy to infer missing entity types. Extensive experiments show the strong performance of MCLET compared to the state-of-the-art</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.800</identifier>
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%0 Conference Proceedings
%T Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
%A Hu, Zhiwei
%A Basulto, Victor
%A Xiang, Zhiliang
%A Li, Ru
%A Pan, Jeff
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hu-etal-2023-multi
%X Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an entity into its representation. However, they ignore the semantic knowledge provided by the way in which types can be clustered together. In this paper, we propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing MCLET, which effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings. MCLET is composed of three modules: i) Multi-view Generation and Encoder module, which encodes structured information from entity-type, entity-cluster and cluster-type views; ii) Cross-view Contrastive Learning module, which encourages different views to collaboratively improve view-specific representations of entities and types; iii) Entity Typing Prediction module, which integrates multi-head attention and a Mixture-of-Experts strategy to infer missing entity types. Extensive experiments show the strong performance of MCLET compared to the state-of-the-art
%R 10.18653/v1/2023.emnlp-main.800
%U https://aclanthology.org/2023.emnlp-main.800
%U https://doi.org/10.18653/v1/2023.emnlp-main.800
%P 12950-12963

Markdown (Informal)

[Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs](https://aclanthology.org/2023.emnlp-main.800) (Hu et al., EMNLP 2023)

• Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs (Hu et al., EMNLP 2023)

ACL

• Zhiwei Hu, Victor Basulto, Zhiliang Xiang, Ru Li, and Jeff Pan. 2023. Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12950–12963, Singapore. Association for Computational Linguistics.