Learning Explainable Logical Rules through Graph Embedding

DOI
  • PHUA Yin Jun
    SOKENDAI (The Graduate University for Advanced Studies) National Institute of Informatics
  • INOUE Katsumi
    SOKENDAI (The Graduate University for Advanced Studies) National Institute of Informatics

Bibliographic Information

Other Title
  • 説明可能な論理規則のグラフ埋め込みによる学習

Description

<p>Recent years have seen the surge in machine learning applications within various fields. As practitioners seeks to utilize machine learning methods in areas that affect our day-to-day lives, accountability and verification is still seen as the largest obstacle to mass adoption. Despite research advancements in the interpretability of deep learning models, the massive amount of rules generated by these methods do not allow a human to understand the models any better. To allow better understanding of huge and complex logic programs, we propose a method that utilizes graph embedding to cluster the atoms and simplifies the resulting program. We perform several experiments to prove the effectiveness of our method, and also show that the resulting program is much easier to read and understand than the original program.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390003825189448064
  • NII Article ID
    130007857021
  • DOI
    10.11517/pjsai.jsai2020.0_3e1gs202
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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