From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures

This paper introduces a post-hoc explainable AI method tailored for Knowledge Graph Embedding models. These models are essential to Knowledge Graph Completion yet criticized for their opaque, black-box nature. Despite their significant success in capturing the semantics of knowledge graphs through high-dimensional latent representations, their inherent complexity poses substantial challenges to explainability. Unlike existing methods, our approach directly decodes the latent representations encoded by Knowledge Graph Embedding models, leveraging the principle that similar embeddings reflect similar behaviors within the Knowledge Graph. By identifying distinct structures within the subgraph neighborhoods of similarly embedded entities, our method identifies the statistical regularities on which the models rely and translates these insights into human-understandable symbolic rules and facts. This bridges the gap between the abstract representations of Knowledge Graph Embedding models and their predictive outputs, offering clear, interpretable insights. Key contributions include a novel post-hoc explainable AI method for Knowledge Graph Embedding models that provides immediate, faithful explanations without retraining, facilitating real-time application even on large-scale knowledge graphs. The method's flexibility enables the generation of rule-based, instance-based, and analogy-based explanations, meeting diverse user needs. Extensive evaluations show our approach's effectiveness in delivering faithful and well-localized explanations, enhancing the transparency and trustworthiness of Knowledge Graph Embedding models.