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Institut
In recent years ontologies have become common on the WWW to provide high-level descriptions of specific domains. These descriptions could be effectively used to build applications with the ability to find implicit consequences of their represented knowledge. The W3C developed the Resource Description Framework RDF, a language to describe the semantics of the data on the web, and the Ontology Web Language OWL, a family of knowledge representation languages for authoring ontologies. In this thesis we propose an ontology API engineering framework that makes use of the state-of-the-art ontology modeling technologies as well as of software engineering technologies. This system simplifies the design and implementation process of developing dedicated APIs for ontologies. Developers of semantic web applications usually face the problem of mapping entities or complex relations described in the ontology to object-oriented representations. Mapping complex relationship structures that come with complex ontologies to a useful API requires more complicated API representations than does the mere mapping of concepts to classes. The implementation of correct object persistence functions in such class representations also becomes quite complex.
Existing tools for generating application programming interfaces (APIs) for ontologies lack sophisticated support for mapping the logics-based concepts of the ontology to an appropriate object-oriented implementation of the API. Such a mapping has to overcome the fundamental differences between the semantics described in the ontology and the pragmatics, i.e., structure, functionalities, and behavior implemented in the API. Typically, concepts from the ontology are mapped one-to-one to classes in the targeted programming language. Such a mapping only produces concept representations but not an API at the desired level of granularity expected by an application developer. We present a Model-Driven Engineering (MDE) process to generate customized APIs for ontologies. This API generation is based on the semantics defined in the ontology but also leverages additional information the ontology provides. This can be the inheritance structure of the ontology concepts, the scope of relevance of an ontology concept, or design patterns defined in the ontology.
Hybrid automata are used as standard means for the specification and analysis of dynamical systems. Several researches have approached them to formally specify reactive Multi-agent systems situated in a physical environment, where the agents react continuously to their environment. The specified systems, in turn, are formally checked with the help of existing hybrid automata verification tools. However, when dealing with multi-agent systems, two problems may be raised. The first problem is a state space problem raised due to the composition process, where the agents have to be parallel composed into an agent capturing all possible behaviors of the multi-agent system prior to the verification phase. The second problem concerns the expressiveness of verification tools when modeling and verifying certain behaviors. Therefore, this paper tackles these problems by showing how multi-agent systems, specified as hybrid automata, can be modeled and verified using constraint logic programming(CLP). In particular, a CLP framework is presented to show how the composition of multi-agent behaviors can be captured dynamically during the verification phase. This can relieve the state space complexity that may occur as a result of the composition process. Additionally, the expressiveness of the CLP model flexibly allows not only to model multi-agent systems, but also to check various properties by means of the reachability analysis. Experiments are promising to show the feasibility of our approach.
The processing of data is often restricted by contractual and legal requirements for protecting privacy and IPRs. Policies provide means to control how and by whom data is processed. Conditions of policies may depend on the previous processing of the data. However, existing policy languages do not provide means to express such conditions. In this work we present a formal model and language allowing for specifying conditions based on the history of data processing. We base the model and language on XACML.
Texture-based text detection in digital images using wavelet features and support vector machines
(2010)
In dieser Bachelorarbeit wird ein neues texturbasiertes Verfahren zur Detektion von Texten in digitalen Bildern vorgestellt. Das Verfahren kann im wesentlichen in zwei Hauptaufgaben unterteilt werden, in Detektion von Textblöcken und Detektion von einzelnen Wörtern, wobei die einzelnen Wörter aus den detektierten Textblöcken extrahiert werden. Im Groben agiert das entwickelte Verfahren mit mehreren Support Vector Machines, die mit Hilfe von waveletbasierten Merkmalen mögliche Textregionen eines Bildes zu wirklichen Textregionen klassiffzieren. Die möglichen Textregionen werden dabei durch unterschiedlich ausgerichtete Kantenprojektionen bestimmt. Das Resultat des Verfahrens sind X/Y Koordinaten, Breite und Höhe von rechteckigen Regionen eines Bildes, die einzelne Wörter enthalten. Dieses Wissen kann weiterverarbeitet werden, beispielsweise durch eine Texterkennungssoftware, um an die wichtigen und sehr nützlichen Textinformationrneines Bildes zu gelangen.
Unlocking the semantics of multimedia presentations in the web with the multimedia metadata ontology
(2010)
The semantics of rich multimedia presentations in the web such as SMIL, SVG and Flash cannot or only to a very limited extend be understood by search engines today. This hampers the retrieval of such presentations and makes their archival and management a difficult task. Existing metadata models and metadata standards are either conceptually too narrow, focus on a specific media type only, cannot be used and combined together, or are not practically applicable for the semantic description of rich multimedia presentations. In this paper, we propose the Multimedia Metadata Ontology (M3O) for annotating rich, structured multimedia presentations. The M3O provides a generic modeling framework for representing sophisticated multimedia metadata. It allows for integrating the features provided by the existing metadata models and metadata standards. Our approach bases on Semantic Web technologies and can be easily integrated with multimedia formats such as the W3C standards SMIL and SVG. With the M3O, we unlock the semantics of rich multimedia presentations in the web by making the semantics machine-readable and machine-understandable. The M3O is used with our SemanticMM4U framework for the multi-channel generation of semantically-rich multimedia presentations.
Die Entwicklung von Algorithmen im Sinne des Algorithm Engineering geschieht zyklisch. Der entworfene Algorithmus wird theoretisch analysiert und anschließend implementiert. Nach der praktischen Evaluierung wird der Entwurf anhand der gewonnenen Kenntnisse weiter entwickelt. Formale Verifffizierung der Implementation neben der praktischen Evaluierung kann den Entwicklungsprozess verbessern. Mit der Java Modeling Language (JML) und dem KeY tool stehen eine einfache Spezififfkationssprache und ein benutzerfreundliches, automatisiertes Verififfkationstool zur Verfügung. Diese Arbeit untersucht, inwieweit das KeY tool für die Verifffizierung von komplexeren Algorithmen geeignet ist und welche Rückmeldungen für Algorithmiker aus der Verififfkation gewonnen werden können.Die Untersuchung geschieht anhand von Dijkstras Algorithmus zur Berechnung von kürzesten Wegen in einem Graphen. Es sollen eine konkrete Implementation des Standard-Algorithmus und anschließend Implementationen weiterer Varianten verifffiziert werden. Dies ahmt den Entwicklungsprozess des Algorithmus nach, um in jeder Iteration nach möglichen Rückmeldungen zu suchen. Bei der Verifffizierung der konkreten Implementation merken wir, dass es nötig ist, zuerst eine abstraktere Implementation mit einfacheren Datenstrukturen zu verififfzieren. Mit den dort gewonnenen Kenntnissen können wir dann die Verifikation der konkreten Implementation fortführen. Auch die Varianten des Algorithmus können dank der vorangehenden Verififfkationen verifiziert werden. Die Komplexität von Dijkstras Algorithmus bereitet dem KeY tool einige Schwierigkeiten bezüglich der Performanz, weswegen wir während der Verifizierung die Automatisierung etwas reduzieren müssen. Auf der anderenrn Seite zeigt sich, dass sich aus der Verifffikation einige Rückmeldungen ableiten lassen.