Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach

Abstract

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance.

Publication
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD, September 18 -22 2023, Turin, Italy (CORE A)
Zicheng Zhao
Zicheng Zhao
PhD Student @ NJUST (09/2023-)

My research interests mainly focus on the areas of artificial intelligence and data mining, especially for the graph neural network and knowledge graph.

Linhao Luo
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
Shirui Pan
Professor and ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.