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Online Learning of Weighted Relational Rules for Complex Event Recognition

  • Nikos KatzourisEmail author
  • Evangelos Michelioudakis
  • Alexander Artikis
  • Georgios Paliouras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

Abstract

Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time. Code related to this paper is available at: https://github.com/nkatzz/OLED.

Keywords

Online structure and weight learning Markov logic networks Event calculus 

Notes

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780754.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nikos Katzouris
    • 1
    Email author
  • Evangelos Michelioudakis
    • 1
    • 2
  • Alexander Artikis
    • 1
    • 3
  • Georgios Paliouras
    • 1
  1. 1.National Center for Scientific Research (NCSR) “Demokritos”AthensGreece
  2. 2.National and Kapodistrian University of AthensAthensGreece
  3. 3.University of PireausPireausGreece

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