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.

DOI

https://doi.org/10.31274/etd-180810-5060

Copyright Owner

Yingbei Tong

Language

en

File Format

application/pdf

File Size

30 pages

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