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

KGE models are widely used but poorly understood: Knowledge Graph Embedding models successfully capture complex relationships in data but operate as "black boxes," making it difficult for users to understand why they make specific predictions—a critical gap for responsible AI deployment.

The new method decodes hidden patterns without slowing systems down: KGEPrisma explains model decisions by directly translating the abstract numerical representations into human-readable rules and facts, offering explanations immediately without requiring expensive computational retraining, unlike existing methods like Kelpie.

Explanations are grounded in how the model actually works: The method identifies the specific graph neighborhoods and statistical patterns that models rely on for predictions, meaning explanations reflect the model's actual reasoning rather than post-hoc approximations, improving trustworthiness.

Interpretability enables better oversight of AI systems: By converting opaque embeddings into symbolic structures that humans can understand, the approach allows stakeholders to audit model decisions, identify potential biases, and verify that systems are making predictions for the right reasons—essential for responsible AI governance.

The approach is practical for real-world deployment: Because explanations are generated locally (for individual predictions) and don't require model modification, this method can be applied to existing Knowledge Graph systems already in use, making responsible AI more accessible without costly system overhauls.