Faithfully Explaining Predictions of Knowledge Embeddings
Gustavo Polleti, Fábio Gagliardi Cozman · 2019
Knowledge embeddings are key ingredients of advanced question-answering and recommender systems. Even though their predictions are accurate, they are rather hard to interpret by human users; interpretability techniques are needed so as to provide meaningful human-friendly explanations for prediction generated by embeddings. We propose a novel model-agnostic method inspired by local surrogate approaches that generates faithful explanations for knowledge embedding predictions.