Affiliations: Computer Science, Drexel University, Philadelphia, PA, USA. E-mail: santi@cs.drexel.edu | Artificial Intelligence Research Institute (IIIA), Spanish Council for Scientific Research, Bellaterra, Spain. E-mail: enric@iiia.csic.es
Note: [] Corresponding author: Santiago Ontañón, Computer Science, Drexel University, Philadelphia, PA 19104, USA. E-mail: santi@cs.drexel.edu
Abstract: We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi-relational patterns called properties. Using them, we perform a property-based re-representation of relational examples that facilitates the development of relational learning techniques. Additionally, we show the usefulness of re-representation with a collection of experiments in the context of nearest neighbor classification.
