Knowledge Representation and Reasoning

Intelligent systems are knowledge-based. They rely on formally defined knowledge about the domain of interest, spcified in a knowledge base using logical languages. This allows for using different inference mechanism to derive implicit information from a knowledge base. Furthermore, such algorithms can automatically detect inconsistencies and modelling errors, which is used to assist users when building a knoweldge base. 

Prof. Dr. Birte Glimm and Dr. Kazakov represent this area within the institute. The main research focus lies on the development of automated reasoning algorithms and optimisations. These are implemented in tools such as ELK, Konclude, or HermiT. Questions regarding the complexity and the efficient evaluation of ontological query languages constitute a further research topic. Members of the group were actively involved in the development of the Web Ontology Language OWL 2 and the SPARQL 1.1 query language standards within the World Wide Web Consortium (W3C). 

Reasoners of the institute are very successful at the OWL Reasoner Evaluation Competitions. In 2014 and 2015 our reasoners  ELK und Konclude won all six categories. In 2013 our reasoners won 7 out of 10 categories. 

Contact

email: Birte.Glimm(at)uni-ulm.de
phone: +49 (0)731/50-24125
fax:     +49 (0)731/50-24119

Postal Address

Birte Glimm
University of Ulm
Institute of Artificial Intelligence
D-89069 Ulm

Office

James-Franck-Ring
building O27, level 4
room 448

Students' Theses in the area of Knowledge Representation and Reasoning

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Opitz, Michael
Query Engine für OWL 2 RL Datenbanken
Master Thesis
Ulm University,
2014
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Optiz, Michael
Optimierung und Parallelisierung eines Schlussfolgerungssystems für OWL 2 RL
Bachelor Thesis
Ulm University,
2011
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Publications in the area of Knowledge Representation and Reasoning

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Liebig, Thorsten; Opitz, Michael
Reasoning over Dynamic Data in Expressive Knowledge Bases with Rscale
Proceedings of Workshop on Ordering and Reasoning (OrdRing 2011)
2011
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