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Annotations of multimedia documents typically have been pursued in two different directions. Either previous approaches have focused on low level descriptors, such as dominant color, or they have focused on the content dimension and corresponding annotations, such as person or vehicle. In this paper , we present a software environment to bridge between the(More)
In this paper, a knowledge representation infrastructure for semantic multimedia content analysis and reasoning is presented. This is one of the major objectives of the aceMedia Integrated Project where ontolo-gies are being extended and enriched to include low-level audiovisual features, descriptors and behavioural models in order to support automatic(More)
In this paper, an ontology infrastucture for multimedia reasoning is presented, making it possible to combine low-level visual de-scriptors with domain specific knowledge and subsequently analyze multimedia content with a generic algorithm that makes use of this knowledge. More specifically, the ontology infrastructure consists of a domain-specific(More)
Effective management and exploitation of multimedia documents requires extraction of the underlying semantics. Multimedia analysis algorithms can produce fairly rich but imprecise information about a multimedia document. In this paper, a multimedia reasoning architecture is presented using the fuzzy extension of expressive SHIN , f-SHIN. First a(More)
The effective management and exploitation of multimedia documents requires the extraction of the underlying semantics. Multimedia analysis algorithms can produce fairly rich, though imprecise information about a multimedia document which most of the times remains unexploited. In this paper we propose a methodology for semantic indexing and retrieval of(More)
We present a novel approach to representing uncertain information in ontologies based on design patterns. We provide a brief description of our approach, present its use in case of fuzzy information and probabilistic information, and describe the possibility to model multiple types of uncertainty in a single ontology. We also shortly present an appropriate(More)
Fuzzy Description Logics (fuzzy DLs) are extensions of classic DLs that are capable of representing and reasoning with imprecise and vague knowledge. Though reasoning algorithms for very expressive fuzzy DLs have been developed and optimizations have started to be explored, the efficiency of such systems is still questionable, and the study of tractable(More)
Sophisticated uncertainty representation and reasoning are necessary for the alignment and integration of Web data from different sources. For this purpose the extension of the Description Logics using fuzzy set theory has been proposed, resulting to fuzzy Description Logics (DLs). However, despite the fact that since the initial proposal a lot of work has(More)
An important problem for the success of ontology-based applications is how to provide persistent storage and querying. For that purpose, many RDF tools capable of storing and querying over a knowledge base, have been proposed. Recently, fuzzy extensions to ontology languages have gained considerable attention especially due to their ability to handle vague(More)