The scarcity of manually labeled data for supervised machine learning methods presents a significant limitation on their ability to acquire knowledge. The use of kernels in Support Vector Machines (SVMs) provides an excellent mechanism to introduce prior knowledge into the SVM learners, such as by using unlabeled text or existing ontologies as additional knowledge sources. Our aim is to develop three kernels