Abstract
Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which aims to predict missing facts for unseen relations with few-shot associated facts, has attracted increasing attention from practitioners and researchers. However, existing FKGC methods are based on metric learning or meta-learning, which often suffer from out-of-distribution and overfitting problems. Meanwhile, they are incompetent at estimating the uncertainty, which is critically important as model predictions could be very unreliable in few-shot setting. Furthermore, most of them cannot handle complex relations and ignore path information in KGs, which largely limits their performance. In this paper, we propose a novel normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC). Specifically, we unify the normalizing flow and neural process to model the complex distribution of KG completion functions. This offers a novel way to predict facts for few-shot relations while estimating the uncertainty in predictions. Then we propose a stochastic ManifoldE decoder to incorporate the neural process and handle complex relations in the few-shot setting. To further improve performance, we introduce an attentive relation path-based graph neural network to capture path information in KGs. Extensive experiments on three public datasets demonstrate that our method significantly outperforms the existing FKGC methods and achieves the state-of-the-art performance.
Linhao Luo
PhD Student @ Monash (02/2022-)
My research interests mainly focus on the areas of artificial intelligence and data mining, especially for the graph neural network and recommendation.
Shirui Pan
Professor and ARC Future Fellow
My research interests include data mining, machine learning, and graph analysis.