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Building Ontology from Knowledge Base Systems

In the last decade, ontologies have been considered as the backbone technology in most knowledge-based applications. As ontologies have become more common, their applicability has ranged from artificial intelligence areas such as knowledge representation and natural language processing to different fields such as information integration and retrieval systems, requirements analysis, and lately in semantic web applications. In the literature, several methodologies and methods have been introduced for building ontologies. Some of these methods allow the development of ontologies from existing ontologies or data sources. However, the proposed method for building ontologies integrates different data mining techniques to assist in developing a given domain ontology. Thus, the extracted and representative rules generated from the original dataset can be utilised in developing ontology elements. The main research hypothesis in this chapter is that ontology can be developed from discovered hidden and interesting rules. In order to practically investigate this assumption, this chapter presents a complete developing discovery structure using one of the well known breast cancer test sets. The chapter is organised into five sections. A general overview is found in section two with a brief description of the main components of this research. The development engine framework is introduced in the section three. Section four demonstrates proposed method using Wisconsin Breast Cancer dataset as a case study. Finally, this practical investigation ends by presenting the learned lessons and conclusions.