  Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer
long-tail data-poor relations, which has drawn increasing attention these years
due to its practicalities. The pre-training of previous methods needs to
manually construct the meta-relation set, leading to numerous labor costs.
Self-supervised learning (SSL) is treated as a solution to tackle the issue,
but still at an early stage for FS-KGR task. Moreover, most of the existing
methods ignore leveraging the beneficial information from aliasing relations
(AR), i.e., data-rich relations with similar contextual semantics to the target
data-poor relation. Therefore, we proposed a novel Self-Supervised Learning
model by leveraging Aliasing Relations to assist FS-KGR, termed SARF.
Concretely, four main components are designed in our model, i.e., SSL reasoning
module, AR-assisted mechanism, fusion module, and scoring function. We first
generate the representation of the co-occurrence patterns in a generative
manner. Meanwhile, the representations of aliasing relations are learned to
enhance reasoning in the AR-assist mechanism. Besides, multiple strategies,
i.e., simple summation and learnable fusion, are offered for representation
fusion. Finally, the generated representation is used for scoring. Extensive
experiments on three few-shot benchmarks demonstrate that SARF achieves
state-of-the-art performance compared with other methods in most cases.
