Britz, KHeidema, JMeyer, T2010-01-112010-01-112009-12Britz, K, Heidema, J and Meyer, T. 2009. Modelling object typicality in description logics. 22nd Australasian Joint Conference on Artificial Intelligence (AI'09), Melbourne, Australia, 1-4 December 2009, pp 506-516978-3-642-10438-10302-9743www.springerlink.com.http://hdl.handle.net/10204/3872Ann E. Nicholson, Xiaodong Li (Eds.): AI 2009: Advances in Artificial Intelligence, 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings. Lecture Notes in Computer Science, Vol 5866 Springer 2009.The paper was first presented at the 22 International Workshop on Description Logics (DL 2009), Oxford, UK, 27-30 July 2009The authors present a semantic model of typicality of concept members in description logics (DLs) that accords well with a binary, globalist cognitive model of class membership and typicality. The authors define a general preferential semantic framework for reasoning with object typicality in DLs. They propose the use of feature vectors to rank concept members according to their defining and characteristic features, which provides a modelling mechanism to specify typicality in composite concepts.enObject typicalityDescription logicsPreferential semanticsArtificial intelligenceConference PresentationBritz, K., Heidema, J., & Meyer, T. (2009). Modelling object typicality in description logics. Springer Verlag. http://hdl.handle.net/10204/3872Britz, K, J Heidema, and T Meyer. "Modelling object typicality in description logics." (2009): http://hdl.handle.net/10204/3872Britz K, Heidema J, Meyer T, Modelling object typicality in description logics; Springer Verlag; 2009. http://hdl.handle.net/10204/3872 .TY - Conference Presentation AU - Britz, K AU - Heidema, J AU - Meyer, T AB - The authors present a semantic model of typicality of concept members in description logics (DLs) that accords well with a binary, globalist cognitive model of class membership and typicality. The authors define a general preferential semantic framework for reasoning with object typicality in DLs. They propose the use of feature vectors to rank concept members according to their defining and characteristic features, which provides a modelling mechanism to specify typicality in composite concepts. DA - 2009-12 DB - ResearchSpace DP - CSIR KW - Object typicality KW - Description logics KW - Preferential semantics KW - Artificial intelligence LK - https://researchspace.csir.co.za PY - 2009 SM - 978-3-642-10438-1 SM - 0302-9743 T1 - Modelling object typicality in description logics TI - Modelling object typicality in description logics UR - http://hdl.handle.net/10204/3872 ER -