Tackling the class-imbalance learning problem in semantic web knowledge bases

Abstract

In the Semantic Web context, procedures for deciding the class-membership of an individual to a target concept in a knowledge base are generally based on automated reasoning. However, frequent cases of incompleteness/inconsistency due to distributed, heterogeneous nature and the Web-scale dimension of the knowledge bases. It has been shown that resorting to models induced from the data may offer comparably effective and efficient solutions for these cases, although skewness in the instance distribution may affect the quality of such models. This is known as class-imbalance problem. We propose a machine learning approach, based on the induction of Terminological Random Forests, that is an extension of the notion of Random Forest to cope with this problem in case of knowledge bases expressed through the standard Web ontology languages. Experimentally we show the feasibility of our approach and its effectiveness w.r.t. related methods, especially with imbalanced datasets.