Simplifying RDF Data for Graph-Based Machine Learning.

P. Bloem, A. Wibisono, G.K.D de Vries

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


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.
Original languageEnglish
Title of host publicationKNOW@ LOD
Publication statusPublished - 2014


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