The Integration of Artificial Intelligence and Ontologies: Transformations in Knowledge Representation and Application

Abstract

Artificial Intelligence (AI) is reshaping the landscape of knowledge representation. There is an increasingly strong bidirectional relationship, between AI techniques and ontologies. AI techniques revolutionized traditional, manual ontology development and contribute to automated ontology construction, while ontologies enhance the performance of AI systems and their semantic accuracy. Through a comprehensive review of current literature, this paper aims to examine: i) how Machine Learning (ML) techniques contribute to the automated construction, refinement, and validation of ontologies; ii) the most widely used and effective ML approaches for ontology construction; iii) how domain-specific requirements influence the selection and adaptation of AI techniques for building and applying ontologies; iv) how ontologies enhance the interpretability, explainability, and reliability of AI. This overview highlights the integration between AI and ontology engineering across different domains and indicates that so far successful AI-ontology integration typically follows a collaborative model, in which AI acts as an intelligent assistant to human experts, combining computational efficiency with critical domain knowledge. Ethical concerns, such as bias and hallucinations, remain pressing challenges that require standardized frameworks and careful considerations. However, this reciprocal relationship between AI and ontologies points to the development of more dynamic, adaptive, and complete ontologies.