Classification Analysis in Complex Online Social Networks Using Semantic Web Technologies

The Semantic Web enables people and computers to interact and exchange information. Based on Semantic Web technologies, different machine learning applications have been designed. Particularly important is the possibility to create complex metadata descriptions for any problem domain, based on pre-defined ontologies. In this paper we evaluate the use of a semantic similarity measure based on pre-defined ontologies as an input for a classification analysis in the context of social network analysis. A link prediction between actors of two real world social networks is performed, which could serve as a recommendation system. The social networks involve different types of relations and nodes. We measure the prediction performance based on a semantic similarity measure as well as traditional approaches. The findings demonstrate that the prediction accuracy based on the semantic similarity is comparable to traditional approaches and shows that data mining on complex social networks using ontology-based metadata can be considered as a very promising approach.