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        <title>Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion</title>
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    <name type="personal">
        <namePart type="given">Yongxue</namePart>
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        <namePart type="given">Jie</namePart>
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    <abstract>In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks—head entity prediction, relation prediction, and tail entity prediction—we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR (MRR: 66.6%→ 72.2%, Hit@1: 58.7%→67.0%).</abstract>
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        <date>2024</date>
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%0 Journal Article
%T Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion
%A Shan, Yongxue
%A Zhou, Jie
%A Peng, Jie
%A Zhou, Xin
%A Yin, Jiaqian
%A Wang, Xiaodong
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F shan-etal-2024-multi
%X In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks—head entity prediction, relation prediction, and tail entity prediction—we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR (MRR: 66.6%→ 72.2%, Hit@1: 58.7%→67.0%).
%R 10.1162/tacl_a_00686
%U https://aclanthology.org/2024.tacl-1.57/
%U https://doi.org/10.1162/tacl_a_00686
%P 1027-1042

Markdown (Informal)

[Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion](https://aclanthology.org/2024.tacl-1.57/) (Shan et al., TACL 2024)

• Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion (Shan et al., TACL 2024)

ACL

• Yongxue Shan, Jie Zhou, Jie Peng, Xin Zhou, Jiaqian Yin, and Xiaodong Wang. 2024. Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion. Transactions of the Association for Computational Linguistics, 12:1027–1042.