Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples

Adrianna Janik, Luca Costabello

Keywords: explainable ML, link prediction, knowledge graph embeddings

Abstract: This article addresses the challenge of explaining link predictions in Knowledge Graph Embedding (KGE) models. We propose an example-based approach that leverages the latent space representation of nodes and edges in a knowledge graph to generate explanations. By analyzing the impact of identified triples on model performance, we demonstrate the effectiveness of our approach in generating explanations compared to existing baselines. We evaluate our method on two publicly available datasets and show its superiority in terms of explanatory power for KGE models.

Submission Type: Extended abstract (max 4 main pages).

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Submission Number: 174