  The SemanticWeb emerged as an extension to traditionalWeb, towards adding
meaning (semantics) to a distributed Web of structured and linked data. At its
core, the concept of ontology provides the means to semantically describe and
structure information and data and expose it to software and human agents in a
machine and human-readable form. For software agents to be realized, it is
crucial to develop powerful artificial intelligence and machine learning
techniques, able to extract knowledge from information and data sources and
represent it in the underlying ontology. This survey aims to provide insight
into key aspects of ontology-based knowledge extraction, from various sources
such as text, images, databases and human expertise, with emphasis on the task
of feature selection. First, some of the most common classification and feature
selection algorithms are briefly presented. Then, selected methodologies, which
utilize ontologies to represent features and perform feature selection and
classification, are described. The presented examples span diverse application
domains, e.g., medicine, tourism, mechanical and civil engineering, and
demonstrate the feasibility and applicability of such methods.
