Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion

• URL: http://arxiv.org/abs/2209.01205v1
• Date: Fri, 2 Sep 2022 17:57:03 GMT
• Title: Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
• Authors: Han Wu, Jianyuan Guo, Bala Rajaratnam, Jie Yin
• Abstract summary: We propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information, HiRe can effectively learn and refine the meta representation of few-shot relations. Experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods.
• Score: 25.905974480733562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverage of KGs. It aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have mostly focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, valuable 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 the meta representation of few-shot relations, and consequently generalize very well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods.

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