Special Issue on Ontologies and Data Management: Part I
Thomas Schneider, Mantas Šimkus · Künstliche Intell. · 2020
Modern information systems rely on a large amount of data that is often unstructured, heterogeneous, and/or incomplete. In order to align and complete data, these systems often use ontologies for representing taxonomies and background knowledge. Ontologies have been used in the Knowledge Representation (KR) subfield of Artificial Intelligence (AI) since the 1970s. They have various applications in (bio)medicine, the life sciences, linguistics, the geo-sciences, and the semantic web. Systems that use ontologies do not only access the represented knowledge, but also draw inferences, a process known as automated reasoning. Among existing ontology languages, the family of description logics (DLs) plays an important role because DLs usually provide a good balance between expressive power and decidability/complexity of the various reasoning tasks, also and especially those tasks relevant for data access. The standardized ontology language OWL 2 recommended by the World Wide Web Consortium (W3C) is based on DLs. However, there are further suitable ontology languages, such as existential rules.