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    <abstract>The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity‘s neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained Language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the Knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity‘s description, related triplet, and tuples; In the Curriculum Contrastive Learning part, we define the difficulty of the course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task.</abstract>
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%0 Conference Proceedings
%T Knowledge Graph Entity Typing with Curriculum Contrastive Learning
%A Wang, Hao
%A Nuo, Minghua
%A Jiang, Shan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-knowledge
%X The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity‘s neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained Language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the Knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity‘s description, related triplet, and tuples; In the Curriculum Contrastive Learning part, we define the difficulty of the course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task.
%U https://aclanthology.org/2025.coling-main.38/
%P 574-583

Markdown (Informal)

[Knowledge Graph Entity Typing with Curriculum Contrastive Learning](https://aclanthology.org/2025.coling-main.38/) (Wang et al., COLING 2025)

• Knowledge Graph Entity Typing with Curriculum Contrastive Learning (Wang et al., COLING 2025)

ACL

• Hao Wang, Minghua Nuo, and Shan Jiang. 2025. Knowledge Graph Entity Typing with Curriculum Contrastive Learning. In Proceedings of the 31st International Conference on Computational Linguistics, pages 574–583, Abu Dhabi, UAE. Association for Computational Linguistics.