  Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict
missing links for unseen entities with few-shot links observed. Previous
methods are limited to transductive scenarios, where entities exist in the
knowledge graphs, so they are unable to handle unseen entities. Therefore,
recent inductive methods utilize the sub-graphs around unseen entities to
obtain the semantics and predict links inductively. However, in the few-shot
setting, the sub-graphs are often sparse and cannot provide meaningful
inductive patterns. In this paper, we propose a novel relational anonymous
walk-guided neural process for few-shot inductive link prediction on knowledge
graphs, denoted as RawNP. Specifically, we develop a neural process-based
method to model a flexible distribution over link prediction functions. This
enables the model to quickly adapt to new entities and estimate the uncertainty
when making predictions. To capture general inductive patterns, we present a
relational anonymous walk to extract a series of relational motifs from
few-shot observations. These motifs reveal the distinctive semantic patterns on
KGs that support inductive predictions. Extensive experiments on typical
benchmark datasets demonstrate that our model derives new state-of-the-art
performance.
