Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

Arthur Colombini Gusmao, A.H. Chaim Correia, Glauber de Bona, Fabio Gagliardi Cozman

Research output: Contribution to conferencePaperAcademic

Abstract

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.
Original languageEnglish
Publication statusPublished - 14 Jul 2018
Event2018 ICML Workshop on Human Interpretability in Machine Learning - Stockholm, Sweden
Duration: 14 Jul 201814 Jul 2018
https://sites.google.com/view/whi2018/home

Workshop

Workshop2018 ICML Workshop on Human Interpretability in Machine Learning
Abbreviated titleWHI
Country/TerritorySweden
CityStockholm
Period14/07/1814/07/18
Internet address

Keywords

  • Artificial Intelligence
  • Machine Learning

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