# Ontology-Based Feature Selection: A Survey
## 4 Ontologies  
The enormous amount of information available in the continuously expanding Web by far exceeds human processing capabilities. This gave rise to the question of whether it is possible to build tools that will automate information retrieval and knowledge extraction from the Web repository. The Semantic Web emerged as a proposed solution to this problem. In its essence, it is an extension to the Web, in which content is represented in such a way that machines are able to process it and infer new knowledge from it. Its purpose is to alleviate the limitations of current knowledge engineering technology with respect to searching, extracting, maintaining, uncovering and viewing information, and support advanced knowledge-based systems. Within the Semantic Web framework, information is organized in conceptual spaces according to its meaning. Automated tools search for inconsistencies and ensure content integrity. Keyword-based search is replaced by knowledge extraction through query answering. In order to realize its vision, the Semantic Web does not rely on \"exotic\" intelligent technology, where agents are able to mimic humans in understanding the predominant HTML content. Rather it approaches the problem from the Web page side. Specifically, it requires Web pages to contain informative (semantic) annotations about their content. These semantics (metadata) enable software to process information without the need to \"understand\" it. The eXtensible Markup Language (XML) was a first step towards this goal. Nowadays, the Resource Description Framework (RDF), RDF Scheme (RDFS) and the Web Ontology Language (OWL) are the main technologies that drive the implementation of the Semantic Web. In general, ontologies are the basic building blocks for inference techniques on the Semantic Web. As stated in W3C's OWL Requirements Documents [44]: \"An ontology defines the terms used to describe and represent an area of knowledge\". Ontological terms are concepts and properties which capture the knowledge of a domain area. Concepts are organized in a hierarchy that expresses the relationships among them by means of superclasses representing higher level concepts, and subclasses representing specific (constrained) concepts. Properties are of two types: those that describe attributes (features) of the concepts, and those that introduce binary relations between the concepts. An example ontology is depicted in Figure 1.  
In order to succeed in the goal to express knowledge in a machine-processable way, an ontology has to exhibit certain characteristics, namely abstractness, preciseness, explicitness, consensus, and domain specificity. An ontology is abstract when it specifies knowledge in a conceptual way. Instead of making statements about specific occurrences of individuals, it tries to cover situations in a conceptual way. Ontologies are expressed in a knowledge representation language that is grounded on formal semantics, i.e., it describes the knowledge rigorously and precisely. Such semantics do not refer to subjective intuitions, nor are they open to different interpretations. Furthermore, knowledge is stated explicitly. Notions that are not directly included in the ontology are not part of the conceptualization it captures. In addition, an ontology reflects a common understanding of domain concepts within a community. In this sense, a prerequisite of an ontology is the existence of social consensus. Finally, it targets a specific domain of interest. The more refined the scope of the domain, the more effective an ontology can be at capturing the details rather than covering a broad range of related topics. The most popular language for engineering ontologies is OWL [45]. OWL (and the latest iteration: OWL2) defines constructs, namely classes, associated properties, and binary relationships between those classes, which can be used to create domain vocabularies along with constructs for expressiveness (e.g., cardinalities, unions, intersections), thus enabling the modeling of complex and rich axioms.