A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Introduction to Algorithms
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Semantic coherence scoring using an ontology
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A mathematical model for context and word-meaning
CONTEXT'03 Proceedings of the 4th international and interdisciplinary conference on Modeling and using context
RelExt: a tool for relation extraction from text in ontology extension
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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Non-statistical natural language understanding components need world knowledge of the domain for which they are applied in a machine-readable form. This knowledge can be represented by manually created ontologies. However, as soon as new concepts, instances or relations are involved in the domain, the manually created ontology lacks necessary information, i.e. it becomes obsolete and/or incomplete. This means its "world model" will be insufficient to understand the user. The scalability of a natural language understanding system, therefore, essentially depends on its capability to be up to date. The approach presented herein applies the information provided by the user in a dialog system to acquire the knowledge needed to understand him or her adequately. Furthermore, it takes the position that the type of incremental ontology learning as proposed herein constitutes a viable approach to enhance the scalability of natural language systems.