Developing Semantic Classifiers for Big Data

When the amount of RDF data is very large, it becomes more likely that the triples describing entities will contain errors and may not include the specification of a class from a known ontology. The work presented here explores the utilization of methods from machine learning to develop classifiers for identifying the semantic categorization of entities based upon the property names used to describe the entity. The goal is to develop classifiers that are accurate, but robust to errors and noise. The training data comes from DBpedia, where entities are categorized by type and densely described with RDF properties. The initial experimentation reported here indicates that the approach is promising.