31,992 research outputs found

    Challenges in Bridging Social Semantics and Formal Semantics on the Web

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    This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

    Towards a Context Knowledge Taxonomy. Combined Methodologies to Improve a Fast-Search Concept Extraction for an Ontology Population

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    Context in Architectural Design can be defined-related-comparable to hypothesis and boundary conditions in mathematics. An eco-system that influences it by means of natural and artificial events, space and time dimension. The research has the aim to analyze the critical issues related to Context by providing a contribution to the study of interactions between Context Knowledge and Architectural Design and how it can be used to improve the performance of the buildings and reducing design mistakes. The research focusing on formal ontologies, has developed a model that enables a semantic approach to design application programs, to manage information, to answer design questions and to have a clear relation between the formal representation of the context domain and its meanings. This context model provides an advancement on the state of the art in simplified design assumptions, in term of ontology ambiguity and complexity reduction, by using algorithms to extract and optimize branches of the graph. The extraction does not limit the number of relations, that can be extended and improve context taxonomy coherency and accuracy

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page
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