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    <abstract>Knowledge Graphs (KGs) often suffer from incomplete knowledge, which which restricts their utility. Recently, Contrastive Learning (CL) has been introduced to Knowledge Graph Completion (KGC), significantly improving the discriminative capabilities of KGC models and setting new benchmarks in performance. However, existing contrastive methods primarily focus on individual triples, overlooking the broader structural connectivities and topologies of KGs. This narrow focus limits a comprehensive understanding of the graph’s structural knowledge. To address this gap, we propose StructKGC, a novel contrastive learning framework designed to flexibly accommodate the diverse topologies inherent in KGs. Additionally, we introduce four contrastive tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level CL, Path-level CL, and Relation composition level CL. These tasks are trained synergistically during the fine-tuning of pre-trained language models (PLMs), allowing for a more nuanced capture of subgraph semantics. To validate the effectiveness of our method, we perform a comprehensive set of experiments on several real-world datasets. The experimental results demonstrate that our approach achieves SOTA performance under standard supervised and low-resource settings. Furthermore, the different levels of structure-aware tasks introduced can mutually reinforce each other, leading to consistent performance improvements.</abstract>
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%0 Conference Proceedings
%T Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning
%A Lin, Jiashi
%A Wang, Lifang
%A Lu, Xinyu
%A Hu, Zhongtian
%A Zhang, Wei
%A Lu, Wenxuan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F lin-etal-2024-improving
%X Knowledge Graphs (KGs) often suffer from incomplete knowledge, which which restricts their utility. Recently, Contrastive Learning (CL) has been introduced to Knowledge Graph Completion (KGC), significantly improving the discriminative capabilities of KGC models and setting new benchmarks in performance. However, existing contrastive methods primarily focus on individual triples, overlooking the broader structural connectivities and topologies of KGs. This narrow focus limits a comprehensive understanding of the graph’s structural knowledge. To address this gap, we propose StructKGC, a novel contrastive learning framework designed to flexibly accommodate the diverse topologies inherent in KGs. Additionally, we introduce four contrastive tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level CL, Path-level CL, and Relation composition level CL. These tasks are trained synergistically during the fine-tuning of pre-trained language models (PLMs), allowing for a more nuanced capture of subgraph semantics. To validate the effectiveness of our method, we perform a comprehensive set of experiments on several real-world datasets. The experimental results demonstrate that our approach achieves SOTA performance under standard supervised and low-resource settings. Furthermore, the different levels of structure-aware tasks introduced can mutually reinforce each other, leading to consistent performance improvements.
%U https://aclanthology.org/2024.emnlp-main.772
%P 13948-13959

Markdown (Informal)

[Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning](https://aclanthology.org/2024.emnlp-main.772) (Lin et al., EMNLP 2024)

• Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning (Lin et al., EMNLP 2024)

ACL

• Jiashi Lin, Lifang Wang, Xinyu Lu, Zhongtian Hu, Wei Zhang, and Wenxuan Lu. 2024. Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13948–13959, Miami, Florida, USA. Association for Computational Linguistics.