Relation extraction is a key step for the problem of rendering a structure from natural language text format. In general, structures are composed by entities and relationships among them. The most successful approaches on relation extraction apply supervised machine learning on hand-labeled corpus for creating highly accurate classifiers. Although good robustness is achieved, hand-labeled corpus are not scalable due to the expensive cost of its creation. In this work we apply an alternative paradigm for creating a considerable number of examples of instances for classification. Such method is called distant supervision. Along with this alternative approach we adopt Semantic Web ontologies to propose and use new features for training classifiers. Those features are based on the structure and semantics described by ontologies where Semantic Web resources are defined. The use of such features has a great impact on the precision and recall of our final classifiers. In this work, we apply our theory on corpus extracted from Wikipedia. We achieve a high precision and recall for a considerable number of relations.