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.

本论文介绍了一种针对知识图嵌入模型的事后可解释人工智能方法，通过直接解码嵌入在知识图中的潜在表示，识别相似嵌入实体的子图邻域内的不同结构，并将这些见解转化为人类可理解的符号规则和事实，在知识图嵌入模型的抽象表示和预测输出之间构建了桥梁，提供清晰、可解释的见解。