Papers › OSL𝛼: Online Structure Learning Using Background Knowledge Axiomatization

OSL𝛼: Online Structure Learning Using Background Knowledge Axiomatization

4 Sep 2016European Conference on Machine Learning and Knowledge Discovery in Databases 2016 9archive 2025-07-28

Evangelos Michelioudakis, Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis

We present OSL𝛼 — 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 (MLN−EC) as background knowledge and a benchmark dataset for video surveillance.

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