A well known drawback in building machine learning semantic relation detectors for natural language is the availability of a large number of qualified training instances for the target relations. This work presents an automatic approach to build multilingual semantic relation detectors through distant supervision combining the two largest resources of structured and unstructured content available on the Web, the DBpedia and the Wikipedia resources. We map the DBpedia ontology back to the Wikipedia to extract more than 100.000 training instances for more than 90 DBpedia relations for English and Portuguese without human intervention. First, we mine the Wikipedia articles to find candidate instances for relations described at DBpedia ontology. Second, we preprocess and normalize the data filtering out irrelevant instances. Finally, we use the normalized data to construct SVM detectors. The experiments performed on the English and Portuguese baselines shows that the lexical and syntactic features extracted from Wikipedia texts combined with the semantic features extracted from DBpedia can significantly improve the performance of relation detectors. For English language, the SVM detector was trained in a corpus formed by 90 DBpedia relations and 42.471 training instances, achieving 81.08 per cent of F-Measure when applied to a test set formed by 28.773 instances. The Portuguese detector was trained with 50 DBpedia relations and 200 examples by relation, being evaluated in 81.91 per cent of F-Measure in a test set containing 18.333 instances. A Relation Extraction (RE) process has many distinct steps that usually begins with text pre-processing and finish with the training and the evaluation of relation detectors. Therefore, this works not only presents an RE approach but also an architecture of a framework that supports the implementation and the experiments of a RE process.