Ontology-based Feature Selection: A Survey

TL;DR

This survey reviews ontology-based feature selection methods that leverage semantic structures to improve knowledge extraction from diverse data sources across various application domains.

Contribution

It provides a comprehensive overview of ontology-driven feature selection techniques and their applications in different fields, highlighting recent methodologies and challenges.

Findings

Ontology-based methods enhance feature selection accuracy.

Applications span medicine, tourism, engineering, demonstrating versatility.

Ontology utilization improves knowledge extraction from heterogeneous data.

Abstract

The SemanticWeb emerged as an extension to the traditional Web, towards adding meaning 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.