Intelligent systems require access to general knowledge about the world and to specific knowledge about the application domain in which they are deployed. The aim of the knowledge representation and engineering topic area of ScaDS.AI Dresden/Leipzig is to develop suitable languages and formalisms that can be used to represent this knowledge, along with associated reasoning methods and algorithms. Based on these methods, we seek to engineer practical tools that support knowledge engineers in capturing actual knowledge in concrete applications and that make it possible to integrate symbolic knowledge representation into the complex and heterogeneous systems of modern AI.

Research Focus

At ScaDS.AI Dresden/Leipzig, we carry out cutting-edge research in various aspects of knowledge representation and engineering such as ontology languages, with an emphasis on description logics and rule-based languages, ontology reasoning and algorithms, ontology-mediated access to large and incomplete data, both exact and approximate, knowledge graphs and Wikidata, non-monotonic reasoning, formal argumentation, explanation of the behaviour of knowledge-based systems, and methods for dealing with heterogeneous and diverse knowledge.

Aims

• Advance the theoretical foundations of ontologies, knowledge representation, knowledge graphs and argumentation frameworks
• Develop methods for explaining the results of knowledge-based AI systems
• Integrate symbolic AI methods with approaches to AI based on machine learning
• Advance the development and deployment of knowledge graphs such as Wikidata
• Provide methods and tools for AI-based access to large and heterogeneous data