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Babelnet text classification5/8/2023 Regarding this issue, the current analysis suggests that the above motivations are mainly based on the fact that an ontology provides structured knowledge representation as well as measures of semantic similarity. The second issue investigates the motivations for building an ontology in order to perform 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. The first issue refers to the application areas of ontology-based feature selection. Several research issues related to the use of ontologies in feature selection for classification problems are investigated. Furthermore, developing ontology-based feature selection methods for achieving real-time analysis and prediction regarding high-dimensional datasets remains a key challenge. 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. 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. Keyword-based search is replaced by knowledge extraction through semantic query answering. Automated tools search for inconsistencies and ensure content integrity. Within the Semantic Web framework, information is organized in conceptual spaces according to its meaning. The goal is to alleviate the limitations of current knowledge engineering technology with respect to searching, extracting, maintaining, uncovering, and viewing information, supporting advanced knowledge-based systems. In its essence, it is an extension to the traditional Web, where content is now represented in such a way that machines are able to process it (machine-processable) and infer new knowledge out of it. The Semantic Web emerged as a technological solution to this problem. This problem has been transformed to the research question of whether it is possible to develop methods and tools that will automate the retrieval of information and the extraction of knowledge from Web repositories. The vast amount of information available in the continuously expanding Web by far exceeds human processing capabilities. 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. 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. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. First, common classification and feature selection algorithms are presented. 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. 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. 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. The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information.
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