Search

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., Comment: This paper is a part of the book titled Compendium of Neuro-Symbolic Artificial Intelligence which can be found at the following link: https://www.iospress.com/ catalog/books/compendium-of-neurosymbolic-artificial-intelligence

The article presents author's views on the role of mass media in China, as the freedom for the media does not exist in a communist country. It informs that mass media is always considered as the mouthpiece of the government in the country. It states that mass media was used as an instrument for the extension of the views of the political party that rules the country related to the both domestic and foreign policy.