Predicting the Quality of Semantic Relations by Applying Machine Learning Classifiers

In this paper, we propose the application of Machine Learning (ML) methods to the Semantic Web (SW) as a mechanism to predict the correctness of semantic relations. For this purpose, we have acquired a learning dataset from the SW and we have performed an extensive experimental evaluation covering more than 1,800 relations of various types. We have obtained encouraging results, reaching a maximum of 74.2% of correctly classified semantic relations for classifiers able to validate the correctness of multiple types of semantic relations (generic classifiers) and up to 98% for classifiers focused on evaluating the correctness of one particular semantic relation (specialized classifiers).