Simplifying RDF Data for Graph-Based Machine Learning
Peter Bloem, A. Wibisono, Gerben Klaas Dirk de Vries · VU Research Portal · 2014
Abstract. From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for-mat can add complexity and obfuscate the internal structure. We in-vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre-processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas-sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets. 1