arXiv:2406.01759v1 Announce Type: new
Abstract: 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.
Analysis of Post-Hoc Explainable AI Method for Knowledge Graph Embedding Models
Knowledge Graph Embedding (KGE) models have played a crucial role in Knowledge Graph Completion by capturing the semantics of knowledge graphs through latent representations. However, the opaque and black-box nature of these models has been a major criticism. This paper introduces a post-hoc explainable AI method specifically designed for KGE models, aiming to provide transparent, interpretable insights.
One of the key challenges in achieving explainability for KGE models lies in understanding the complex latent representations that encode the relationships within the knowledge graph. Existing methods have focused on visualizations or indirect explanations, but this approach takes a different approach.
The proposed method leverages the principle that similar embeddings reflect similar behaviors within the knowledge graph. By decoding the latent representations and identifying distinct structures within subgraph neighborhoods of similarly embedded entities, the method uncovers statistical regularities that the KGE models rely on. These regularities are then translated into human-understandable symbolic rules and facts.
What makes this method unique is its ability to bridge the gap between the abstract representations of KGE models and their predictive outputs. It provides immediate, faithful explanations without requiring retraining of the models, which is a significant advantage for real-time applications, especially on large-scale knowledge graphs.
The multi-disciplinary nature of this work is evident through its integration of AI, knowledge representation, and graph analysis. It combines techniques from machine learning and graph theory to extract interpretable insights from KGE models.
Furthermore, this method offers flexibility in generating different types of explanations based on user needs. It can produce rule-based explanations, which provide generalizable patterns and insights, instance-based explanations, which offer specific explanations for individual entities, and analogy-based explanations, which identify similarities and differences between different entities.
Extensive evaluations of the proposed method demonstrate its effectiveness in delivering faithful and well-localized explanations. By enhancing the transparency and trustworthiness of KGE models, this method has the potential to address the concerns of stakeholders who require interpretable AI systems. It could be particularly valuable in domains such as healthcare, finance, and recommendation systems where explainability is crucial.
In conclusion, this post-hoc explainable AI method for Knowledge Graph Embedding models represents a significant step towards addressing the explainability challenges in knowledge graph completion. By directly decoding latent representations and translating them into human-understandable symbolic rules and facts, this method offers a transparent, interpretable approach without sacrificing the predictive power of KGE models. Its multi-disciplinary nature and flexibility make it a promising avenue for future research and application in various domains.