# Ontology-Based Feature Selection: A Survey
###### Abstract  
The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, 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 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, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic. feature selection; ontology; text classification; machine-learning +
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Consequently, identification of high-quality features from an ontological hierarchy plays a significant role in the ability to extract information from documents. The main research domain where ontologies have been employed in terms of selecting specific features is text classification, where predefined categories are associated with free-text unstructured documents based on their content. The continuous increase of volumes of text documents on the Web makes text classification an important tool for searching information. Due to their enormous scale in terms of the number of classes, training examples, features, and feature dependencies, text classification applications present considerable research challenges. In standard feature selection approaches, feature representation and selection are the main tasks prior to the classification, whereas in the ontology-based feature selection approaches, the task of feature extraction and selection from the input data based on a data-to-ontology mapping is required. This paper presents related work on the problem of feature representation and selection based on ontologies in the context of knowledge extraction from documents, databases, and human expertise. Beyond important issues related to the volume, velocity, variety, and veracity (4 V) of the Web of (Big) data, the presented work has been motivated by a number of open issues and challenges that keep this research topic still active, especially in the era of Knowledge Graphs (KG) and Linked Open Data (LOD), where bias at different levels (data, schema, reasoning) may cause the development of \"unfair\" models in different application domains. Furthermore, developing ontology-based feature selection methods for achieving real-time analysis and prediction regarding high-dimensional datasets remains a key challenge. Several research issues related to the use of ontologies in feature selection for classification problems are investigated. The first issue refers to the application areas of ontology-based feature selection. This survey concentrates on a wide range of application areas such as document classification, opinion mining, selection of manufacturing processes, recommendation systems, urban management, and information security, where certain algorithmic structures are discussed, depending on the application framework.