Summary of From Latent to Lucid: Transforming Knowledge Graph Embeddings Into Interpretable Structures, by Christoph Wehner and Chrysa Iliopoulou and Tarek R. Besold

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

by Christoph Wehner, Chrysa Iliopoulou, Tarek R. Besold

First submitted to arxiv on: 3 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper presents a novel post-hoc explainable AI method for Knowledge Graph Embedding (KGE) models, addressing their opaque nature. KGE models are crucial for Knowledge Graph Completion but criticized for being black-boxes. The proposed approach directly decodes latent representations encoded by KGE models, leveraging the principle that similar embeddings reflect similar behaviors within the knowledge graph. By identifying distinct structures in subgraph neighborhoods of similarly embedded entities, the method identifies statistical regularities on which KGE models rely and translates these insights into human-understandable symbolic rules and facts. This bridges the gap between abstract representations and predictive outputs, offering clear and interpretable insights. The paper’s key contributions include a novel post-hoc explainable AI method for KGE models that provides immediate and faithful explanations without retraining, facilitating real-time application on large-scale knowledge graphs. The method’s flexibility enables generation of rule-based, instance-based, and analogy-based explanations, meeting diverse user needs. Extensive evaluations demonstrate the approach’s effectiveness in delivering faithful and well-localized explanations, enhancing transparency and trustworthiness of KGE models.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper makes a new kind of AI model more understandable. These models are important for completing knowledge graphs, but they work in mysterious ways. The authors created a way to explain what these models are doing without having to retrain them from scratch. This new approach can be used on big knowledge graphs and gives different types of explanations that people might find helpful. The authors tested their method and showed that it works well.