989 resultados para RDF,Named Graphs,Provenance,Semantic Web,Semantics


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Orientador: António Jorge Cardoso

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All over the world, organizations are becoming more and more complex, and there’s a need to capture its complexity, so this is when the DEMO methodology (Design and Engineering Methodology for Organizations), created and developed by Jan L. G. Dietz, reaches its potential, which is to capture the structure of business processes in a coherent and consistent form of diagrams with their respective grammatical rules. The creation of WAMM (Wiki Aided Meta Modeling) platform was the main focus of this thesis, and had like principal precursor the idea to create a Meta-Editor that supports semantic data and uses MediaWiki. This prototype Meta-Editor uses MediaWiki as a receptor of data, and uses the ideas created in the Universal Enterprise Adaptive Object Model and the concept of Semantic Web, to create a platform that suits our needs, through Semantic MediaWiki, which helps the computer interconnect information and people in a more comprehensive, giving meaning to the content of the pages. The proposed Meta-Modeling platform allows the specification of the abstract syntax i.e., the grammar, and concrete syntax, e.g., symbols and connectors, of any language, as well as their model types and diagram types. We use the DEMO language as a proofof-concept and example. All such specifications are done in a coherent and formal way by the creation of semantic wiki pages and semantic properties connecting them.

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The sharing of knowledge and integration of data is one of the biggest challenges in health and essential contribution to improve the quality of health care. Since the same person receives care in various health facilities throughout his/her live, that information is distributed in different information systems which run on platforms of heterogeneous hardware and software. This paper proposes a System of Health Information Based on Ontologies (SISOnt) for knowledge sharing and integration of data on health, which allows to infer new information from the heterogeneous databases and knowledge base. For this purpose it was created three ontologies represented by the patterns and concepts proposed by the Semantic Web. The first ontology provides a representation of the concepts of diseases Secretariat of Health Surveillance (SVS) and the others are related to the representation of the concepts of databases of Health Information Systems (SIS), specifically the Information System of Notification of Diseases (SINAN) and the Information System on Mortality (SIM)

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One of the current challenges of Ubiquitous Computing is the development of complex applications, those are more than simple alarms triggered by sensors or simple systems to configure the environment according to user preferences. Those applications are hard to develop since they are composed by services provided by different middleware and it is needed to know the peculiarities of each of them, mainly the communication and context models. This thesis presents OpenCOPI, a platform which integrates various services providers, including context provision middleware. It provides an unified ontology-based context model, as well as an environment that enable easy development of ubiquitous applications via the definition of semantic workflows that contains the abstract description of the application. Those semantic workflows are converted into concrete workflows, called execution plans. An execution plan consists of a workflow instance containing activities that are automated by a set of Web services. OpenCOPI supports the automatic Web service selection and composition, enabling the use of services provided by distinct middleware in an independent and transparent way. Moreover, this platform also supports execution adaptation in case of service failures, user mobility and degradation of services quality. The validation of OpenCOPI is performed through the development of case studies, specifically applications of the oil industry. In addition, this work evaluates the overhead introduced by OpenCOPI and compares it with the provided benefits, and the efficiency of OpenCOPI s selection and adaptation mechanism

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Typically Web services contain only syntactic information that describes their interfaces. Due to the lack of semantic descriptions of the Web services, service composition becomes a difficult task. To solve this problem, Web services can exploit the use of ontologies for the semantic definition of service s interface, thus facilitating the automation of discovering, publication, mediation, invocation, and composition of services. However, ontology languages, such as OWL-S, have constructs that are not easy to understand, even for Web developers, and the existing tools that support their use contains many details that make them difficult to manipulate. This paper presents a MDD tool called AutoWebS (Automatic Generation of Semantic Web Services) to develop OWL-S semantic Web services. AutoWebS uses an approach based on UML profiles and model transformations for automatic generation of Web services and their semantic description. AutoWebS offers an environment that provides many features required to model, implement, compile, and deploy semantic Web services

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With the advance of the Cloud Computing paradigm, a single service offered by a cloud platform may not be enough to meet all the application requirements. To fulfill such requirements, it may be necessary, instead of a single service, a composition of services that aggregates services provided by different cloud platforms. In order to generate aggregated value for the user, this composition of services provided by several Cloud Computing platforms requires a solution in terms of platforms integration, which encompasses the manipulation of a wide number of noninteroperable APIs and protocols from different platform vendors. In this scenario, this work presents Cloud Integrator, a middleware platform for composing services provided by different Cloud Computing platforms. Besides providing an environment that facilitates the development and execution of applications that use such services, Cloud Integrator works as a mediator by providing mechanisms for building applications through composition and selection of semantic Web services that take into account metadata about the services, such as QoS (Quality of Service), prices, etc. Moreover, the proposed middleware platform provides an adaptation mechanism that can be triggered in case of failure or quality degradation of one or more services used by the running application in order to ensure its quality and availability. In this work, through a case study that consists of an application that use services provided by different cloud platforms, Cloud Integrator is evaluated in terms of the efficiency of the performed service composition, selection and adaptation processes, as well as the potential of using this middleware in heterogeneous computational clouds scenarios

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The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

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Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

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Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.

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Esta exposición pretende ser una introducción al estudio de un amplio, complejo y dinámico conjunto de nociones, técnicas y prácticas sociales, que gira en torno a la blogosfera, “un vigoroso subespacio de comunicación en Internet”, tal como lo denomina Sáez Vacas en esta misma revista. El objetivo no es tanto ser exhaustivo en el tratamiento, como dar a conocer al lector los distintos conceptos y fenómenos involucrados en la génesis de este peculiar universo, cuyo origen podemos situar en un metafórico Blog Bang. Hablaremos de los blogs (weblogs o bitácoras), su origen, caracterización, clasificación y cuantificación, de la tecnología que los rodea y de conceptos relacionados, tales como los wikis, el socialware, la blogocultura y la web semántica. This essay is designed as an introduction to the study of a broad, complex and dynamic set of notions, techniques and social practices revolving around the blogosphere –“an intense communication subspace on the Internet”, as defined by Saéz Vacas in this magazine. The aim of this article is not to exhaustively cover the topic, but rather, to introduce the reader to the different concepts and phenomena involved in the genesis of this peculiar universe, whose origin lies in the metaphoric Blog Bang. We will touch on blogs (weblogs and bitcores), their origin, nature, classification and quantification, the technology that surrounds them, and other related concepts like wikis, socialware, blogculture and web semantics.

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In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

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