Towards Bi-Level Out-of-Distribution Logical Reasoning on Knowledge Graphs

Fatimah Alotaibi, Dawei Zhou

Abstract: Logical reasoning is crucial in knowledge graphs, enabling the discovery of latent knowledge and facilitating various downstream tasks such as question-answering and knowledge discovery. Despite the promising results of state-of-the-art methods, their limited generalization capabilities in handling out-of-distribution (OOD) queries and KG data pose a significant challenge. This challenge arises from the complex variations in logical queries and the dynamic nature of graphs, resulting in two OOD scenarios at the query and KG data levels. To address this challenge, we propose a novel, generic framework that handles bi-level OOD logical reasoning on KGs by jointly modeling OOD queries and OOD KG data. First, we start with the uncertainty representation module to handle the KG-level OOD. Then, we further design an adversarial learning module to efficiently improve reasoning over query-level OOD. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework in handling these two levels of OOD scenarios.