Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

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...

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Bibliographic Details
Published in:arXiv.org 2018-06
Main Authors: Arthur Colombini Gusmão, Alvaro Henrique Chaim Correia, Glauber De Bona, Fabio Gagliardi Cozman
Format: Article
Language:English
Subjects:
Embedding
Knowledge base
Model accuracy
Natural language processing
Neural networks
Pedagogy
Semantic web
Online Access:Get full text
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Summary: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.
ISSN:2331-8422