Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks

• Published in: Inductive Logic Programming Vol. 10326; pp. 14 - 26
• Main Authors: Malec, Marcin, Khot, Tushar, Nagy, James, Blask, Erik, Natarajan, Sriraam
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 3319633414; 9783319633411
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-63342-8_2

Statistical Relational Learning (SRL) approaches have been developed to learn in presence of noisy relational data by combining probability theory with first order logic. While powerful, most learning approaches for these models do not scale well to large datasets. While advances have been made on using relational databases with SRL models [14], they have not been extended to handle the complex model learning (structure learning task). We present a scalable structure learning approach that combines the benefits of relational databases with search strategies that employ rich inductive bias from Inductive Logic Programming. We empirically show the benefits of our approach on boosted structure learning for Markov Logic Networks.