Date of Award
Master of Science
Markov logic networks (MLNs) are a statistical relational model that incorporates first- order logic and probability by attaching weights to first-order clauses. However, due to the large search space, the structure learning of MLNs is a computationally expensive problem. In this paper, we present a new algorithm for learning the structure of Markov Logic Network by directly utilizing the data to construct the candidate clauses. Our approach makes use of a Markov Network learning algorithm to construct a template network. We then apply the template to guide the candidate clauses construction process. The experimental results demonstrate that our algorithm is promising.
Tong, Yingbei, "Learning Markov Logic Network Structure by Template Constructing" (2017). Graduate Theses and Dissertations. 15441.