N-ary facts composed of a primary triple (head entity, relation, tail entity) and an arbitrary number of auxiliary attribute-value pairs, are prevalent in real-world knowledge graphs (KGs). Link prediction on n-ary facts is to predict a missing element in an n-ary fact. This helps populate and enrich KGs and further promotes numerous downstream applications. Previous studies usually require a substantial amount of high-quality data to understand the elements in n-ary facts. However, these studies overlook few-shot relations, which have limited labeled instances, yet are common in real-world scenarios. Thus, this paper introduces a new task, few-shot link prediction on n-ary facts. It aims to predict a missing entity in an n-ary fact with limited labeled instances. We further propose a model for Few-shot Link prEdict on N-ary facts, thus called FLEN, which consists of three modules: the relation learning, support-specific adjusting, and query inference modules. FLEN captures relation meta information from limited instances to predict a missing entity in a query instance. To validate the effectiveness of FLEN, we construct three datasets based on existing benchmark data. Our experimental results show that FLEN significantly outperforms existing related models in both few-shot link prediction on n-ary facts and binary facts.

翻译：由主三元组（头实体、关系、尾实体）及任意数量辅助属性-值对构成的N元事实在现实知识图谱中普遍存在。N元事实上的链接预测旨在预测N元事实中的缺失元素，这有助于扩充并丰富知识图谱，进而推动众多下游应用。以往研究通常需要大量高质量数据来理解N元事实中的元素，但这些研究忽略了小样本关系——这类关系仅有少量标注实例，却在现实场景中普遍存在。为此，本文提出一项新任务：面向N元事实的小样本链接预测，其目标是在仅有少量标注实例的情况下预测N元事实中的缺失实体。我们进一步提出面向N元事实的小样本链接预测模型FLEN（Few-shot Link prEdict on N-ary facts），该模型包含三个模块：关系学习模块、支持集自适应调整模块和查询推理模块。FLEN从少量实例中捕获关系元信息，进而预测查询实例中的缺失实体。为验证FLEN的有效性，我们基于现有基准数据构建了三个数据集。实验结果表明，FLEN在N元事实与二元事实的小样本链接预测任务上均显著优于现有相关模型。