Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods

Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. The paper extends this idea to the Semantic Web by introducing the novel SPARQL-ML approach to perform data mining for Semantic Web data. The approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. The paper analyzes the approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. The analytical results show that the approach can be used for any Semantic Web data set to perform instance-based learning and classification.