A model for building probabilistic knowledge-based systems using divergence distances
作者：
Highlights：
• Propose an architecture of a probabilistic KBS that meets the need of the consistency assurance and the compliance with probability rules.
• Build a mathematical model for the merging problem that takes into account inconsistency levels, the structural diversity of probabilistic knowledge bases, and ensure the consistency for the joint probabilistic knowledge base.
• Prove the reliability of the proposed merging model in both the theoretical and experimental aspect.
• Investigate a family of merging operators with a large range of divergence distance functions between probability distributions.
摘要
•Propose an architecture of a probabilistic KBS that meets the need of the consistency assurance and the compliance with probability rules.•Build a mathematical model for the merging problem that takes into account inconsistency levels, the structural diversity of probabilistic knowledge bases, and ensure the consistency for the joint probabilistic knowledge base.•Prove the reliability of the proposed merging model in both the theoretical and experimental aspect.•Investigate a family of merging operators with a large range of divergence distance functions between probability distributions.
论文关键词：Probabilistic knowledge base,Divergence distance function,Merging algorithm,Probabilistic knowledge-based system
论文评审过程：Received 28 August 2020, Revised 20 November 2020, Accepted 10 December 2020, Available online 16 December 2020, Version of Record 14 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114494
