Faithfully Explaining Predictions of Knowledge Embeddings

• Gustavo Polleti Universidade de São Paulo
• Fábio Cozman Universidade de São Paulo

DOI: https://doi.org/10.5753/eniac.2019.9343

Resumo

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.

Palavras-chave: Machine Learning, Data Mining, Knowledge Embeddings

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