Dibowski, HenrikSchmid, StefanReussner, Ralf H.Koziolek, AnneHeinrich, Robert2021-01-272021-01-272021978-3-88579-701-2https://dl.gi.de/handle/20.500.12116/34726Knowledge graphs as fundamental pillar of artificial intelligence are experiencing a strong demand. In contrast to machine learning and deep learning, knowledge graphs do not require large amounts of (training) data and offer a bigger potential for a multitude of domains and problems. This article shows the application of knowledge graphs for the semantic description and management of data in a data lake, which improves the findability and reusability of data, and enables the automatic processing by algorithms. Since knowledge graphs contain both the data as well as its semantically described schema (ontology), they enable novel ontology-driven software architectures, in which the domain knowledge and business logic can completely reside on the knowledge graph level. This article further introduces such a use case: an ontology-driven frontend implementation, which is able to fully adapt itself based on the underlying knowledge graph schema and dynamically render information in the desired manner.enArtificial IntelligenceOntologyKnowledge RepresentationKnowledge GraphSemantic Data LakeData CatalogSemantic SearchSemantic LayerOntology-Driven UIs10.18420/inf2020_021617-5468