Recently, the vision of a semantic web in which background knowledge is stored through the use of machine-processable metadata has been attained. Ontologies have shown to be the answer for structuring background knowledge by providing a formal and complex vocabulary of a particular domain that is shared by a group of people. The manual building of ontologies is a tedious task, which can easily result in a knowledge acquisition bottleneck. However, the success of the semantic web depends on the proliferation of ontologies, which requires that the engineering of ontologies be completed quickly and easily. This book explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in this book includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in order to support a semi-automatic ontology engineering process. This text is designed for researchers and developers of semantic web applications. It could also serve as a supplemental reference to advanced level courses in ontologies and the semantic web.