Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion

• URL: http://arxiv.org/abs/2209.01205v4
• Date: Tue, 03 Jun 2025 10:39:21 GMT
• Title: Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
• Authors: Han Wu, Jie Yin, Bala Rajaratnam, Jianyuan Guo,
• Abstract summary: Few-shot KG completion aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference.<n>We propose a hierarchical relational learning method (HiRe) for few-shot KG completion.
• Score: 24.57087752710105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) are powerful in terms of their inference abilities, but are also notorious for their incompleteness and long-tail distribution of relations. To address these challenges and expand the coverage of KGs, few-shot KG completion aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have focused on designing local neighbor aggregators to learn entity-level information and/or imposing a potentially invalid sequential dependency assumption at the triplet level to learn meta relation information. However, pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine meta representations of few-shot relations, and thus generalize well to new unseen relations. Extensive experiments on benchmark datasets validate the superiority of HiRe over state-of-the-art methods. The code can be found in https://github.com/alexhw15/HiRe.git.

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