Degree Type

Thesis

Date of Award

2017

Degree Name

Master of Science

Department

Computer Science

Major

Computer Science

First Advisor

Jin Tian

Abstract

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.

Copyright Owner

Yingbei Tong

Language

en

File Format

application/pdf

File Size

30 pages

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