One-shot knowledge graph completion based on disentangled representation learning

Abstract

One-shot knowledge graph completion (KGC) aims to infer unseen facts when only one support entity pair is available for a particular relationship. Prior studies learn reference representations from one support pair for matching query pairs. This strategy can be challenging, particularly when dealing with multiple relationships between identical support pairs, resulting in indistinguishable reference representations. To this end, we propose a disentangled representation learning framework for one-shot KGC. Specifically, to learn sufficient representations, we construct an entity encoder with a fine-grained attention mechanism to explicitly model the input and output neighbors. We adopt an orthogonal regularizer to promote the independence of learned factors in entity representation, enabling the matching processor with max pooling to adaptively identify the semantic roles associated with a particular relation. Subsequently, the one-shot KGC is accomplished by seamlessly integrating the aforementioned modules in an end-to-end learning manner. Extensive experiments on real-world datasets demonstrate the outperformance of the proposed framework.