Faithfully Explaining Predictions of Knowledge Embeddings

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


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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POLLETI, Gustavo; COZMAN, Fábio. Faithfully Explaining Predictions of Knowledge Embeddings. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 892-903. DOI: