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Knowledge bases are employed in a variety of applications from naturallanguage processing to semantic web search; alas, in practice their usefulnessis hurt by their incompleteness. Embedding models attain state-of-the-artaccuracy in knowledge base completion, but their predictions are notoriouslyhard to interpret. In this paper, we adapt "pedagogical approaches" (from theliterature on neural networks) so as to interpret embedding models byextracting weighted Horn rules from them. We show how pedagogical approacheshave to be adapted to take upon the large-scale relational aspects of knowledgebases and show experimentally their strengths and weaknesses.

Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach