Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Updating logical databases
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
A Probabilistic Extension to Ontology Language OWL
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
Extending an ontology-based search with a formalism for spatial reasoning
Proceedings of the 2008 ACM symposium on Applied computing
Improving an RCC-Derived Geospatial Approximation by OWL Axioms
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
On the update of description logic ontologies at the instance level
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
GINO – a guided input natural language ontology editor
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Towards a fuzzy description logic for the semantic web (preliminary report)
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Consistent evolution of OWL ontologies
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
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Learning ontologies from large text corpora is a well understood task while evolving ontologies dynamically from user-input has rarely been adressed so far. Evolution of ontologies has to deal with vague or incomplete information. Accordingly, the formalism used for knowledge representation must be able to handle this kind of information. Classical logical approaches such as description logics are particularly poor in adressing uncertainty. Ontology evolution may benefit from exploring probabilistic or fuzzy approaches to knowledge representation. In this thesis an approach to evolve and update ontologies is developed which uses explicit and implicit user-input and extends probabilistic approaches to ontology engineering.