Learning probabilistic rules for answering why-questions

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Why-questions try to get to the bottom of the matter and ask for explanations. In this paper, we show how we can learn the complete structure of probabilistic rules for a question-answering system from example interpretations. These rules are then used by a meta-interpreter to find answers in the form of explanations for why-questions for a particular application domain.
Original languageEnglish
Title of host publicationIMA2019 First International Workshop on Interpretability: Methodologies and Algorithms (IMA2019)
Number of pages9
Publication statusPublished - 2019

Keywords

  • why-questions
  • explainability
  • probabilistic logic programming
  • probabilistic rule learning

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