TitleTransforming graph data for statistical relational learning
Publication TypeJournal Article
Year of Publication2012
AuthorsRossi, RA, McDowell, LK, Aha, DW, Neville, J
JournalJournal of Artificial Intelligence Research
Volume45
Issue1
Pagination363-441
Date Published09/2012
Abstract

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.

Refereed DesignationRefereed
Full Text
NRL Publication Release Number: 
12-1231-3792
pub_tags: 
machine learning
statistical relational learning
preprocessing
suvey
key_pub_tags: