Title: Statistical relational learning
Authors: Blockeel, Hendrik
Issue Date: 2013
Publisher: Springer
Host Document: Handbook of Neural Information Processing pages:241-281
Abstract: Relational learning refers to learning from data that have a complex structure. This structure may be either internal (a data instance may itself have a complex structure) or external (relationships between this instance and other data elements). Statistical relational learning refers to the use of statistical learning methods in a relational learning context, and the challenges involved in that. In this chapter we give an overview of statistical relational learning. We start with some motivating problems, and continue with a general description of the task of (statistical) relational learning and some of its more concrete forms (learning from graphs, learning from logical interpretations, learning from relational databases). Next, we discuss a number of approaches to relational learning, starting with symbolic (non-probabilistic) approaches, and moving on to numerical and probabilistic methods. Methods discussed include inductive logic programming, relational neural networks, and probabilistic logical or relational models.
ISBN: 978-3-642-36656-7
Publication status: published
KU Leuven publication type: IHb
Appears in Collections:Informatics Section

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