Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion

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 contextlevel), 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 . Current few-shot KG methods have, however, focused on designing local neighbor aggregators to learn entity-level information, and/or imposing a sequential assumption at the triplet level to learn meta relation information (See Table 1 ). The potential of leveraging pairwise triplet-level interactions and context-level relational information has been largely unexplored. Published as a conference paper at ICLR 2023 ℎ 𝑡𝑡 … (a) (b) (c) ℎ 1 𝑡𝑡 1 context information triplet ℎ 2 𝑡𝑡 2 ℎ 𝑘𝑘 𝑡𝑡 k Triplet-level relational information ℎ 2 𝑡𝑡 2 Entity-level relational information