Publisher’s description: The KnowledgeSeeker is a useful system to develop various intelligent applications such as ontology-based search engines, ontology-based text classification systems, ontological agent systems, semantic web systems etc. The KnowledgeSeeker contains four different ontological components. First, it defines the knowledge representation model Ontology Graph. Second, it contains an ontology learning process that – based on chi-square statistics – is proposed for automatic learning of ontology graphs from texts in different areas. Third, it defines an ontology generation method that transforms the learning outcome to the ontology graph format for machine processing, and also can be visualized for human validation. Fourth, it defines different ontological operations (such as similarity measurement and text classification) that can be carried out with the use of generated ontology graphs. The main purpose of the KnowledgeSeeker system framework is to improve traditional information systems with higher efficiency. In particular, it can increase the accuracy of text classification systems as well as enhance the search intelligence in search engines. This can be done by enhancing the system with machine processable ontology.