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.

翻译：知识图谱上的小样本关系推理旨在推断长尾数据稀疏关系，因其实际应用价值近年来受到广泛关注。现有方法的预训练过程需要人工构建元关系集合，导致大量人力成本。自监督学习被视为解决该问题的方案，但在小样本关系推理任务中仍处于早期阶段。此外，大多数现有方法忽略了利用别名关系（即与目标数据稀疏关系的上下文语义相似的数据丰富关系）中的有益信息。为此，我们提出一种新型自监督学习模型SARF，通过利用别名关系辅助小样本关系推理。具体而言，模型包含四个核心组件：自监督推理模块、别名关系辅助机制、融合模块和评分函数。我们首先以生成式方式生成共现模式的表示，同时通过别名关系辅助机制学习别名关系的表示以增强推理。此外，提供简单求和与可学习融合等多种表示融合策略。最终将生成的表示用于评分。在三个小样本基准上的大量实验表明，SARF在大多数情况下取得了相较其他方法最优的性能。