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OSLα: Online Structure Learning using background knowledge axiomatization

Michelioudakis Evangelos, Skarlatidis Anastasios, Paliouras, Georgios, Artikis, Alexander

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/90904717-E643-413F-9170-BDE1DA2DBBBD
Έτος 2016
Τύπος Πλήρης Δημοσίευση σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά E. Michelioudakis, A. Skarlatidis, G. Paliouras and A. Artikis, "OSLα: Online Structure Learning using background knowledge axiomatization," in Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016, pp. 232-247. doi: 10.1007/978-3-319-46128-1_15 https://doi.org/10.1007/978-3-319-46128-1_15
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Περίληψη

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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