SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning

• URL: http://arxiv.org/abs/2304.10297v1
• Date: Thu, 20 Apr 2023 13:24:59 GMT
• Title: SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
• Authors: Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou, Yue Liu, Meng Liu, Xihong Yang, Xinwang Liu
• Abstract summary: Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations. We propose a novel Self-Supervised Learning model by leveraging Aliasing Relations to assist FS-KGR, termed SARF.
• Score: 43.59319243928048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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