Our paper “OSLα: Online Structure Learning with Background Knowledge Axiomatization” was accepted at ECML 2016
Evangelos Michelioudakis, Anastasios Skarlatidis, Georgios Paliouras and 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 the irrelational 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.