As more multimedia content is semantically annotated there is the need to fully benefit from the efforts devoted to the annotation. Such benefit is obvious when thinking of standard inference services such as classification and satisfiability, yet we believe there are other, smart, services that can complement basic ones and provide smarter queries on annotated image repositories. In this paper we show and motivate how Concept Abduction and Concept Contraction, recently introduced inference services in Description Logics, can be used for semantic based query and query refinement of annotated images.
Non-standard inferences for knowledge-based image retrieval / Di Noia, T.; Di Sciascio, E.; Donini, F. M.; Di Cugno, F.; Tinelli, E.. - STAMPA. - 11099(2005), pp. 191-197. (Intervento presentato al convegno 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology, EWIMT 2005 tenutosi a London, UK nel 30 November - 1 December 2005) [10.1049/ic.2005.0731].
Non-standard inferences for knowledge-based image retrieval
Di Noia, T.;Di Sciascio, E.;Donini, F. M.;
2005-01-01
Abstract
As more multimedia content is semantically annotated there is the need to fully benefit from the efforts devoted to the annotation. Such benefit is obvious when thinking of standard inference services such as classification and satisfiability, yet we believe there are other, smart, services that can complement basic ones and provide smarter queries on annotated image repositories. In this paper we show and motivate how Concept Abduction and Concept Contraction, recently introduced inference services in Description Logics, can be used for semantic based query and query refinement of annotated images.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
