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Manipulation of deformable linear objects (DLO) has potential applications in aerospace and automotive assembly. The current literature on planning for deformable objects focuses on a single DLO at a time. In this paper, we provid...
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Manipulation of deformable linear objects (DLO) has potential applications in aerospace and automotive assembly. The current literature on planning for deformable objects focuses on a single DLO at a time. In this paper, we provide a problem formulation for attaching a set of interlinked DLOs to a support structure through a set of clamping points. We also present a prototype algorithm that generates a solution in terms of primitive manipulation actions. The algorithm guarantees that none of the interlink constraints are violated. Finally, we incorporate gravity in the computation of a DLO shape and propose a property linking geometrically similar cable shapes across the space of cable length and stiffness. This property allows for computation of solutions for unit length and scaling of the solutions to appropriate length, thus potentially making shape computations faster.
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For data sources to ensure providing reliable linked data, they need to indicate information about the (un)certainty of their data based on the views of their consumers. In Addition, uncertainty information in terms of Semantic We...
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For data sources to ensure providing reliable linked data, they need to indicate information about the (un)certainty of their data based on the views of their consumers. In Addition, uncertainty information in terms of Semantic Web has also to be encoded into a readable, publishable, and exchangeable format to increase the interoperability of systems. This paper introduces a novel approach to evaluate the uncertainty of data in an RDF dataset based on its links with other datasets. We propose to evaluate uncertainty for sets of statements related to user-selected resources by exploiting their similarity interlinks with external resources. Our data-driven approach translates each interlink into a set of links referring to the position of a target dataset from a reference dataset, based on both object and predicate similarities. We show how our approach can be implemented and present an evaluation with real-world datasets. Finally, we discuss updating the publishable uncertainty values.
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摘要 :
For data sources to ensure providing reliable linked data, they need to indicate information about the (un)certainty of their data based on the views of their consumers. In Addition, uncertainty information in terms of Semantic We...
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For data sources to ensure providing reliable linked data, they need to indicate information about the (un)certainty of their data based on the views of their consumers. In Addition, uncertainty information in terms of Semantic Web has also to be encoded into a readable, publishable, and exchangeable format to increase the interoperability of systems. This paper introduces a novel approach to evaluate the uncertainty of data in an RDF dataset based on its links with other datasets. We propose to evaluate uncertainty for sets of statements related to user-selected resources by exploiting their similarity interlinks with external resources. Our data-driven approach translates each interlink into a set of links referring to the position of a target dataset from a reference dataset, based on both object and predicate similarities. We show how our approach can be implemented and present an evaluation with real-world datasets. Finally, we discuss updating the publishable uncertainty values.
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Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implemen...
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Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.
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摘要 :
Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implemen...
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Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semisupervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.
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摘要 :
Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implemen...
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Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.
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摘要 :
Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implemen...
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Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.
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River restoration and rehabilitation work over the past 20 years has largely been carried out at the site level (?be?g and Tapsell, this volume; Holmes and Janes, this volume), and there are very few examples where it has incorpor...
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River restoration and rehabilitation work over the past 20 years has largely been carried out at the site level (?be?g and Tapsell, this volume; Holmes and Janes, this volume), and there are very few examples where it has incorporated an entire river catchment. The Emscher region in Germany is such an example; water quality improvement and river rehabilitation are core elements of a major 30-year economic regeneration and environmental improvement programme for a former heavily industrialized catchment. This chapter provides a brief overview of the effects of industrial growth, the spatial planning and ecological concepts behind the regeneration programme, the institutional structures thathave enabled the work to be carried out, the design of an 'ecological hotspot' and some results from the first 20 years.
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The novel e-Science's datarcentric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, ...
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The novel e-Science's datarcentric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, re-use, and validation of research activities more effective. Scholarly Communication Infrastructures are advocated for bridging such data sources, by offering tools for identification, creation, and navigation of relationships. Since realization and maintenance of such infrastructures is expensive, in this demo we propose a lightweight approach for "preliminary analysis of data source interlinking" to help practitioners at evaluating whether and to what extent realizing them can be effective. We present Data Searchery, a configurable tool enabling users to easily plug-in data sources from different realms with the purpose of cross-relating their objects, be them publications or research data, by identifying relationships between their metadata descriptions.
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摘要 :
The novel e-Science's datarcentric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, ...
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The novel e-Science's datarcentric paradigm has proved that interlinking publications and research data objects coming from different realms and data sources (e.g. publication repositories, data repositories) makes dissemination, re-use, and validation of research activities more effective. Scholarly Communication Infrastructures are advocated for bridging such data sources, by offering tools for identification, creation, and navigation of relationships. Since realization and maintenance of such infrastructures is expensive, in this demo we propose a lightweight approach for "preliminary analysis of data source interlinking" to help practitioners at evaluating whether and to what extent realizing them can be effective. We present Data Searchery, a configurable tool enabling users to easily plug-in data sources from different realms with the purpose of cross-relating their objects, be them publications or research data, by identifying relationships between their metadata descriptions.
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