HIERARCHICAL RELATIONAL LEARNING FOR FEW-SHOT KNOWLEDGE GRAPH COMPLETION

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 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.

1. INTRODUCTION

Knowledge graphs (KGs) comprise a collection of factual triplets, (h, r, t), where each triplet expresses the relationship r between a head entity h and a tail entity t. Large-scale KGs (Vrandečić & Krötzsch, 2014; Mitchell et al., 2018; Suchanek et al., 2007; Bollacker et al., 2008) can provide powerful inference capabilities for many intelligent applications, including question answering (Yao & Van Durme, 2014) , web search (Eder, 2012) and recommendation systems (Wang et al., 2019) . As KGs are often built semi-automatically from unstructured data, real-world KGs are far from complete and suffer from the notorious long-tail problem -a considerable proportion of relations are associated with only very few triplets. As a result, the performance of current KG completion methods significantly degrades when predicting relations with a limited number (few-shot) of training triplets. To tackle this challenge, few-shot KG completion methods have been proposed including GMatching (Xiong et al., 2018) , MetaR(Chen et al., 2019 ), FSRL(Zhang et al., 2020) , FAAN (Sheng et al., 2020) and GANA (Niu et al., 2021) . These methods focus on predicting the missing tail entity t for query triplets by learning from only K reference triplets about a target relation r. Given a target relation r and K reference triplets, K-shot KG completion aims to correctly predict the tail entity t for each query triplet (h, r, ?) using the generalizable knowledge learned from reference triplets. Thus, the crucial aspect of few-shot KG completion is to learn the meta representation of each few-shot relation from a limited amount of reference triplets that can generalize to novel relations. To facilitate the learning of meta relation representations, we identify three levels of relational information (see Figure 1 ). ( 1) At the context level, each reference triplet is closely related to its wider contexts, providing crucial evidence for enriching entity and relation embeddings. 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.



(2) At the triplet level, capturing the commonality among limited reference triplets is essential for learning meta relation representations. (3) At the entity level, the learned meta relation representations should well generalize to unseen query triplets.

