A collaborative learning framework for knowledge graph embedding and reasoning

Knowledge graph embedding (KGE) and knowledge graph reasoning (KGR) aim to automatic completion of knowledge graph (KG). The difference is that most KGE models learn the embedded representation at a triple level. In contrast, KGR models focus more on optimizing decision-making and enhancing the interpretability of reasoning processes with multi-hop paths. As a result, KGE models are better at learning triplet embeddings, whereas KGR models can capture the multihop information between entity pairs. However, KGE and KGR models only focus on one aspect that affects the completion performance. This paper proposes a plug-and-play collaborative learning framework (CLF) for jointly enhancing knowledge graph embedding and reasoning, which can accommodate existing KGR and KGE models. The two models exchange training experiences in this framework to realize mutual learning through a collaborative learning module. In this module, a new distance function is designed to maintain the independence of candidate entities’ probabilities and avoid information loss. Furthermore, a knowledge augmentation module is designed to identify missing key triples to assist in the further iterative training of the framework. Extensive experiments on the benchmark datasets demonstrate that our framework significantly improves the performance of existing models.