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Statistical Relational Artificial Intelligence

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Front. Robot. AI | doi: 10.3389/frobt.2018.00008

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

  • 1University of British Columbia, Canada

The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship among them or combining them. In this paper, we study the relationship between the path ranking algorithm (PRA), one of the most well-known relational learning methods in the graph random walk paradigm, and relational logistic regression (RLR), one of the recent developments in weighted rule learning. We provide a simple way to normalize relations and prove that relational logistic regression using normalized relations generalizes the path ranking algorithm. This result provides a better understanding of relational learning, especially for the weighted rule learning and graph random walk paradigms. It opens up the possibility of using the more flexible RLR rules within PRA models and even generalizing both by including normalized and unnormalized relations in the same model.

Keywords: Statistical relational artificial intelligence, relational learning, Weighted Rule Learning, Graph Random Walk, Relational Logistic Regression, Path Ranking Algorithm

Received: 17 Oct 2017; Accepted: 18 Jan 2018.

Edited by:

Sriraam Natarajan, Indiana University System, United States

Reviewed by:

Elena Bellodi, University of Ferrara, Italy
Nicola Di Mauro, Università degli studi di Bari Aldo Moro, Italy  

Copyright: © 2018 Kazemi and Poole. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Seyed Mehran Kazemi, University of British Columbia, Vancouver, BC, Canada, smkazemi@cs.ubc.ca