Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Comparing formal theories of context in AI
Artificial Intelligence
Metalevel information in ontology-based applications
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A general framework for representing, reasoning and querying with annotated Semantic Web data
Web Semantics: Science, Services and Agents on the World Wide Web
Contextualized knowledge repositories for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
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The capability of dealing with context sensitive knowledge is recognized as a crucial aspect in the management of massive amounts of Semantic Web (SW) data. Contextual knowledge can be modelled either by adopting the primitives from RDF/OWL based SW languages or by extending such languages with new specific constructs for context representation. In this paper, we show the benefits of the context-based solution by comparing modelling and reasoning in the two approaches on the paradigmatic use case of FIFA World Cup. The comparison considers the three key aspects of engineering and exploiting knowledge: (i) simplicity and expressivity of the (formal) language; (ii) compactness of the representation; and (iii) efficiency of reasoning. As for (i), we show that the context-based language enables the construction of simpler and more intuitive models while the RDF/OWL "flat" model presents practical limitations in modelling cross-contextual knowledge. For (ii), we show that the contextualized model is more compact than the OWL based model. Finally for (iii), query answering in the context-based model outperforms in most of the cases performances on the flat model.