Abstract

This paper presents a novel approach to optimize inference in rule-based knowledge systems by introducing a clustering mechanism for rule organization. Rules are clustered using K-Means or Agglomerative Hierarchical Clustering (AHC) algorithms, with different distance measures and clustering strategies. We propose and evaluate four inference strategies based on different group representation methods (mean or median) and rule activation strategies (activation of one or all matching rules). Experimental studies on real knowledge bases show that clustering significantly improves inference performance while maintaining a satisfied inference success rate.

Recommended Citation

Gaibei, I. & Nowak-Brzezińska, A. (2025). Inference processes in rule cluster knowledge base - various approachesIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.63

Paper Type

Poster

DOI

10.62036/ISD.2025.63

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Inference processes in rule cluster knowledge base - various approaches

This paper presents a novel approach to optimize inference in rule-based knowledge systems by introducing a clustering mechanism for rule organization. Rules are clustered using K-Means or Agglomerative Hierarchical Clustering (AHC) algorithms, with different distance measures and clustering strategies. We propose and evaluate four inference strategies based on different group representation methods (mean or median) and rule activation strategies (activation of one or all matching rules). Experimental studies on real knowledge bases show that clustering significantly improves inference performance while maintaining a satisfied inference success rate.