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Extracting Interval Temporal Logic Rules: A First Approach

Authors Davide Bresolin , Enrico Cominato, Simone Gnani, Emilio Muñoz-Velasco , Guido Sciavicco

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Author Details

Davide Bresolin
  • Department of Mathematics, University of Padova, Italy
Enrico Cominato
  • Department of Mathematics, Computer Science and Physics, University of Udine, Italy
Simone Gnani
  • Department of Mathematics and Computer Science, University of Ferrara, Italy
Emilio Muñoz-Velasco
  • Department of Applied Mathematics, University of Malaga, Spain
Guido Sciavicco
  • Department of Mathematics and Computer Science, University of Ferrara, Italy

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Davide Bresolin, Enrico Cominato, Simone Gnani, Emilio Muñoz-Velasco, and Guido Sciavicco. Extracting Interval Temporal Logic Rules: A First Approach. In 25th International Symposium on Temporal Representation and Reasoning (TIME 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 120, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.TIME.2018.7

Abstract

Discovering association rules is a classical data mining task with a wide range of applications that include the medical, the financial, and the planning domains, among others. Modern rule extraction algorithms focus on static rules, typically expressed in the language of Horn propositional logic, as opposed to temporal ones, which have received less attention in the literature. Since in many application domains temporal information is stored in form of intervals, extracting interval-based temporal rules seems the natural choice. In this paper we extend the well-known algorithm APRIORI for rule extraction to discover interval temporal rules written in the Horn fragment of Halpern and Shoham's interval temporal logic.

Subject Classification

ACM Subject Classification
  • Information systems → Clustering and classification
Keywords
  • Interval temporal logic
  • Horn fragment
  • Rule extraction

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