Ontology in Hybrid Intelligence: a concise literature review
Salvatore F. Pileggi
TL;DR
This literature review assesses how Ontology supports Hybrid Intelligence, arguing that ontological methods enhance interoperability, explainability, and system engineering in HI environments. The paper differentiates conceptual versus application-oriented contributions, finding that about 70% of HI studies involving Ontology are application-focused and that many works lack explicit semantic infrastructures. It identifies key qualitative themes—interoperability, explainability, system engineering, and quality/accuracy—and highlights gaps such as unclear HI definitions and insufficient focus on automatic reasoning. The authors advocate for a principled, holistic approach to HI, with Ontology playing a central role in engineering, trust, and dynamic, human–machine collaboration.
Abstract
In a context of constant evolution and proliferation of AI technology,Hybrid Intelligence is gaining popularity to refer a balanced coexistence between human and artificial intelligence. The term has been extensively used in the past two decades to define models of intelligence involving more than one technology. This paper aims to provide (i) a concise and focused overview of the adoption of Ontology in the broad context of Hybrid Intelligence regardless of its definition and (ii) a critical discussion on the possible role of Ontology to reduce the gap between human and artificial intelligence within hybrid intelligent systems. Beside the typical benefits provided by an effective use of ontologies, at a conceptual level, the conducted analysis has pointed out a significant contribution of Ontology to improve quality and accuracy, as well as a more specific role to enable extended interoperability, system engineering and explainable/transparent systems. Additionally, an application-oriented analysis has shown a significant role in present systems (70+% of the cases) and, potentially, in future systems. However, despite the relatively consistent number of papers on the topic, a proper holistic discussion on the establishment of the next generation of hybrid-intelligent environments with a balanced co-existence of human and artificial intelligence is fundamentally missed in literature. Last but not the least, there is currently a relatively low explicit focus on automatic reasoning and inference in hybrid intelligent systems.