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A Probabilistic Soft Logic Reasoning Model with Automatic Rule Learning

  • Jia Zhang
  • Hui ZhangEmail author
  • Bo Li
  • Chunming Yang
  • Xujian Zhao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)

Abstract

Probabilistic Soft Logic (PSL), as a declarative rule-based probability model, has strong extensibility and multi-domain adaptability and has been applied in many domains. In practice, a main difficult is that a lot of common sense and domain knowledge need to be set manually as preconditions for rule establishment, and the acquisition of these knowledge is often very expensive. To alleviate this dilemma, this paper has worked on two aspects: First, a rule automatic learning method was proposed, which combined AMIE+ algorithm and PSL to form a new reasoning model. Second, a multi-level method was used to improve the reasoning efficiency of the model. The experimental results showed that the proposed methods are feasible.

Keywords

Probabilistic soft logic Rules automatically extracted Multi-level approach Machine learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jia Zhang
    • 1
  • Hui Zhang
    • 2
    Email author
  • Bo Li
    • 1
    • 3
  • Chunming Yang
    • 1
  • Xujian Zhao
    • 1
  1. 1.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina
  2. 2.School of ScienceSouthwest University of Science and TechnologyMianyangChina
  3. 3.School of Computer and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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