SCIENCE SPIN-OFFS

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Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

Arthur Colombini Gusmão, Alvaro Henrique Chaim Correia, Glauber De Bona, Fabio Gagliardi Cozman
(Submitted on 20 Jun 2018)

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

https://arxiv.org/abs/1806.09504


ADJECTIVE
relating to teaching.
"innovative pedagogical methods"