Complex aggregates in relational learning

Issue title: Recommender Systems

Article type: Research Article

Authors: Vens, Celine

Affiliations: K.U. Leuven, Department of Computer Science, Celestijnenlaan 200A, 3001 Leuven, Belgium. E-mail: [email protected]

Abstract: In relational learning, one learns patterns from relational databases, which usually contain multiple tables that are interconnected via relations. Thus, an example for which a prediction is to be given may be related to a set of objects that are possibly relevant for that prediction. Relational classifiers differ with respect to how they handle these sets: some use properties of the set as a whole (using aggregation), some refer to properties of specific individuals, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This dissertation describes a learning approach that avoids this bias, using complex aggregates, i.e., aggregates that impose selection conditions on the set to aggregate on.

Keywords: Relational learning, inductive logic programming, aggregation

DOI: 10.3233/AIC-2008-0422

Journal: AI Communications, vol. 21, no. 2-3, pp. 219-220, 2008