Advances in Ontology and the Semantic Web

In the recent years, most of the Artificial Intelligence applications for predictive analysis are based on machine learning and neural network approaches, which rely on models based on an implicit (sub-symbolic) knowledge representation derived from the experience (data-driven). However, many applications for automated reasoning and searching tasks require explicit models based on a human readable (symbolic) representation of rules and properties. Furthermore, symbolic knowledge can also play a relevant role to address the so-called Explainable AI, whose goal is to provide human comprehensible explanations to the decisions taken by a predictive system. In this respect, computational ontologies and the Semantic Web deserve a great interest since they are rooted in formal logic that is at the basis of the representation of symbolic knowledge. This Special Issue aims at presenting methodological and technological advancements, as well as relevant use cases, in the scope of ontologies and the Semantic Web. In particular, high quality contributions are expected in, but not limited to, the areas of: languages for ontology representation; methodologies and tools for ontology engineering; ontology integration; ontology-based reasoning; ontology-based semantic search; data annotation; data integration and interoperability; semantic similarity; semantic relatedness; knowledge graphs; query answering on knowledge graphs; ontologies and sub-symbolic models; logic-based semantics for Explainable AI.