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Large software systems are modularized in order to improve manageability. The parts of a software system communicate in order to achieve the desired functionality. To better understand, develop, manage, and maintain the resulting complexity, this paper presents a framework of ontologies. The ontologies range from very general, foundational ones to ontologies that elucidate the specificities of particular modularization and communication paradigms. We support two specific paradigms. First, we define an ontology for software components that may be used in traditional middleware architectures, e.g., application servers. Second, we specify an ontology for Web services. Through the reuse of existing foundational ontologies and our new Core Software Ontology, our proposal offers several advantages. In particular, it avoids the typical shortcomings related approaches exhibit and it allows for the concise definition of commonalities and differences of the two paradigms. [ABSTRACT FROM AUTHOR]

Knowledge representation and semantic annotation of multimedia documents typically have been pursued in two different directions. Previous approaches have focused either on low-level descriptors, such as dominant colour, or on the semantic content dimension and corresponding manual annotations, such as person or vehicle. Here, a knowledge infrastructure and an experimentation platform for semantic annotation to bridge the two directions are presented. Ontologies are being extended and enriched to include low-level audiovisual features and descriptors. Additionally, a tool that allows for linking low-level MPEG-7 visual descriptions to ontologies and annotations is presented. Thus, ontologies that include prototypical instances of high-level domain concepts together with a formal specification of the corresponding visual descriptors are constructed. This infrastructure is exploited by a knowledge-assisted analysis framework that may handle problems such as segmentation, tracking, feature extraction and matching in order to classify scenes, identify and label objects and thus automatically create the associated semantic metadata. [ABSTRACT FROM AUTHOR]