Application and evaluation of inductive reasoning methods for the semantic web and software analysis

Abstract

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. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting four sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our ap- proach can be used for almost any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines even shows that our approach is superior in terms of classification accuracy.