50,891 research outputs found

    Infraestructura tecnolĂłgica de servicios semĂĄnticos para la Web SemĂĄntica

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    This project aims at creating a network of distributed interoperable semantic services for building more complex ones. These services will be available in semantic Web service libraries, so that they can be invoked by other systems (e.g., semantic portals, software agents, etc.). Thus, to accomplish this objective, the project proposes: a) To create specific technology for developing and composing Semantic Web Services. b) To migrate the WebODE ontology development workbench to this new distributed interoperable semantic service architecture. c) To develop new semantic services (ontology learning, ontology mappings, incremental ontology evaluation, and ontology evolution). d) To develop technological support that eases semantic portal interoperability, using Web services and Semantic Web Services. The project results will be open source, so as to improve their technological transfer. The quality of these results is ensured by a benchmarking process. Keywords: Ontologies and Semantic We

    Building Effective Ontology for Semantic Web: a Discussion Based on Practical Examples

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    The study aims to investigate semantic web and create useful ontology as a teaching and educational tool for others interested in learning more about Semantic web. This paper discussed several emerging issues about the semantic web and ontology building. This paper combines ontology implementation examples with research topics to identify current issues and potential solution in both application and theoretical level. It concludes that although semantic web and ontology technology are not mature enough currently, there is a clear tendency for them to be integrated into various applications to exert synergies

    Evaluation of the Project Management Competences Based on the Semantic Networks

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    The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.

    Ontology learning for the semantic deep web

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    Ontologies could play an important role in assisting users in their search for Web pages. This dissertation considers the problem of constructing natural ontologies that support users in their Web search efforts and increase the number of relevant Web pages that are returned. To achieve this goal, this thesis suggests combining the Deep Web information, which consists of dynamically generated Web pages and cannot be indexed by the existing automated Web crawlers, with ontologies, resulting in the Semantic Deep Web. The Deep Web information is exploited in three different ways: extracting attributes from the Deep Web data sources automatically, generating domain ontologies from the Deep Web automatically, and extracting instances from the Deep Web to enhance the domain ontologies. Several algorithms for the above mentioned tasks are presented. Lxperimeiital results suggest that the proposed methods assist users with finding more relevant Web sites. Another contribution of this dissertation includes developing a methodology to evaluate existing general purpose ontologies using the Web as a corpus. The quality of ontologies (QoO) is quantified by analyzing existing ontologies to get numeric measures of how natural their concepts and their relationships are. This methodology was first applied to several major, popular ontologies, such as WordNet, OpenCyc and the UMLS. Subsequently the domain ontologies developed in this research were evaluated from the naturalness perspective

    Learning from Ontology Streams with Semantic Concept Drift

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    Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing
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