  Knowledge graphs (KGs) store enormous facts as relationships between
entities. Due to the long-tailed distribution of relations and the
incompleteness of KGs, there is growing interest in few-shot knowledge graph
completion (FKGC). Existing FKGC methods often assume the existence of all
entities in KGs, which may not be practical since new relations and entities
can emerge over time. Therefore, we focus on a more challenging task called
inductive few-shot knowledge graph completion (I-FKGC), where both relations
and entities during the test phase are unknown before. Inspired by the idea of
inductive reasoning, we cast I-FKGC as an inductive reasoning problem.
Specifically, we propose a novel Graph Stochastic Neural Process approach
(GS-NP), which consists of two major modules. In the first module, to obtain a
generalized hypothesis (e.g., shared subgraph), we present a neural
process-based hypothesis extractor that models the joint distribution of
hypothesis, from which we can sample a hypothesis for predictions. In the
second module, based on the hypothesis, we propose a graph stochastic
attention-based predictor to test if the triple in the query set aligns with
the extracted hypothesis. Meanwhile, the predictor can generate an explanatory
subgraph identified by the hypothesis. Finally, the training of these two
modules is seamlessly combined into a unified objective function, of which the
effectiveness is verified by theoretical analyses as well as empirical studies.
Extensive experiments on three public datasets demonstrate that our method
outperforms existing methods and derives new state-of-the-art performance.
