\(\mathtt {OSL}\alpha \): Online Structure Learning Using Background Knowledge Axiomatization

  • Evangelos Michelioudakis
  • Anastasios Skarlatidis
  • Georgios Paliouras
  • Alexander Artikis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9851)

Abstract

We present \(\mathtt {OSL}\alpha \)—an online structure learner for Markov Logic Networks (MLNs) that exploits background knowledge axiomatization in order to constrain the space of possible structures. Many domains of interest are characterized by uncertainty and complex relational structure. MLNs is a state-of-the-art Statistical Relational Learning framework that can naturally be applied to domains governed by these characteristics. Learning MLNs from data is challenging, as their relational structure increases the complexity of the learning process. In addition, due to the dynamic nature of many real-world applications, it is desirable to incrementally learn or revise the model’s structure and parameters. Experimental results are presented in activity recognition using a probabilistic variant of the Event Calculus (\(\mathtt {MLN}{\!-\!}\mathtt {EC}\)) as background knowledge and a benchmark dataset for video surveillance.

Keywords

Markov Logic Networks Event Calculus Uncertainty 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Evangelos Michelioudakis
    • 1
    • 2
  • Anastasios Skarlatidis
    • 1
  • Georgios Paliouras
    • 1
  • Alexander Artikis
    • 1
    • 3
  1. 1.Institute of Informatics and Telecommunications, NCSR “Demokritos”AthensGreece
  2. 2.School of Electronic and Computer EngineeringTechnical University of CreteChaniaGreece
  3. 3.Department of Maritime StudiesUniversity of PiraeusPiraeusGreece

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