  Ontologies are used in various domains, with RDF and OWL being prominent
standards for ontology development. RDF is favored for its simplicity and
flexibility, while OWL enables detailed domain knowledge representation.
However, as ontologies grow larger and more expressive, reasoning complexity
increases, and traditional reasoners struggle to perform efficiently. Despite
optimization efforts, scalability remains an issue. Additionally, advancements
in automated knowledge base construction have created large and expressive
ontologies that are often noisy and inconsistent, posing further challenges for
conventional reasoners. To address these challenges, researchers have explored
neuro-symbolic approaches that combine neural networks' learning capabilities
with symbolic systems' reasoning abilities. In this chapter,we provide an
overview of the existing literature in the field of neuro-symbolic deductive
reasoning supported by RDF(S), the description logics EL and ALC, and OWL 2 RL,
discussing the techniques employed, the tasks they address, and other relevant
efforts in this area.
