用于归纳式少样本知识图谱补全的图随机神经过程
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
AI总结：
针对测试时关系与实体均未知的归纳式少样本知识图谱补全，提出GS-NP，通过神经过程提取共享子图假设并以图随机注意力预测与解释，实验达到新SOTA。
摘要：
知识图谱（KGs）以实体之间关系的形式存储大量事实。由于关系的长尾分布以及KGs的不完整性，少样本知识图谱补全（FKGC）正受到越来越多关注。现有FKGC方法通常假设KGs中的所有实体均已存在，但这可能并不现实，因为随着时间推移会出现新的关系和实体。因此，我们关注一项更具挑战性的任务，称为归纳式少样本知识图谱补全（I-FKGC），其中测试阶段的关系和实体在此前均为未知。受归纳推理思想启发，我们将I-FKGC建模为一个归纳推理问题。具体而言，我们提出了一种新颖的图随机神经过程方法（GS-NP），该方法包含两个主要模块。在第一个模块中，为获得泛化假设（例如共享子图），我们提出了一个基于神经过程的假设提取器，对假设的联合分布进行建模，并可从中采样一个假设用于预测。在第二个模块中，基于该假设，我们提出了一个基于图随机注意力的预测器，用于检验查询集中的三元组是否与提取出的假设一致。同时，该预测器能够生成由该假设识别出的解释性子图。最后，这两个模块的训练被无缝结合到一个统一的目标函数中，其有效性通过理论分析和实证研究得到验证。在三个公开数据集上的大量实验表明，我们的方法优于现有方法，并取得了新的最先进性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
