CC BY 4.0Humm, Bernhard G.Bernhard G.HummArcher, PhilPhilArcherBense, HermannHermannBenseBernier, CarolynnCarolynnBernierGoetz, ChristianChristianGoetzHoppe, ThomasThomasHoppeSchumann, FabienneFabienneSchumannSiegel, MelanieMelanieSiegelWenning, RigoRigoWenningZender, AlexanderAlexanderZender2023-01-042023-01-042023https://publica.fraunhofer.de/handle/publica/430582https://doi.org/10.24406/publica-69510.1007/s00287-022-01513-910.24406/publica-695In this article, selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML) are presented: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, and automated machine learning (AutoML). Knowledge-based AI and ML complement each other ideally, as their strengths compensate for the weaknesses of the other discipline. This is demonstrated via selected corporate use cases: anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information.enartificial intelligencemachine learningapplicationssemantic websemantic technologiesjournal article