1,916 research outputs found

    A Framework to Enable the Semantic Inferencing and Querying of Multimedia Content

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    Cultural institutions, broadcasting companies, academic, scientific and defence organisations are producing vast quantities of digital multimedia content. With this growth in audiovisual material comes the need for standardised representations encapsulating the rich semantic meaning required to enable the automatic filtering, machine processing, interpretation and assimilation of multimedia resources. Additionally generating high-level descriptions is difficult and manual creation is expensive although significant progress has been made in recent years on automatic segmentation and low-level feature recognition for multimedia. Within this paper we describe the application of semantic web technologies to enable the generation of high-level, domain-specific, semantic descriptions of multimedia content from low-level, automatically-extracted features. By applying the knowledge reasoning capabilities provided by ontologies and inferencing rules to large, multimedia data sets generated by scientific research communities, we hope to expedite solutions to the complex scientific problems they face

    Towards structured sharing of raw and derived neuroimaging data across existing resources

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    Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery

    Automatic Annotation of Images from the Practitioner Perspective

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    This paper describes an ongoing project which seeks to contribute to a wider understanding of the realities of bridging the semantic gap in visual image retrieval. A comprehensive survey of the means by which real image retrieval transactions are realised is being undertaken. An image taxonomy has been developed, in order to provide a framework within which account may be taken of the plurality of image types, user needs and forms of textual metadata. Significant limitations exhibited by current automatic annotation techniques are discussed, and a possible way forward using ontologically supported automatic content annotation is briefly considered as a potential means of mitigating these limitations

    Exploring manuscripts: sharing ancient wisdoms across the semantic web

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    Recent work in digital humanities has seen researchers in-creasingly producing online editions of texts and manuscripts, particularly in adoption of the TEI XML format for online publishing. The benefits of semantic web techniques are un-derexplored in such research, however, with a lack of sharing and communication of research information. The Sharing Ancient Wisdoms (SAWS) project applies linked data prac-tices to enhance and expand on what is possible with these digital text editions. Focussing on Greek and Arabic col-lections of ancient wise sayings, which are often related to each other, we use RDF to annotate and extract seman-tic information from the TEI documents as RDF triples. This allows researchers to explore the conceptual networks that arise from these interconnected sayings. The SAWS project advocates a semantic-web-based methodology, en-hancing rather than replacing current workflow processes, for digital humanities researchers to share their findings and collectively benefit from each other’s work

    Realizing the Hydrogen Economy through Semantic Web Technologies

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    The FUSION (Fuel Cell Understanding through Semantic Inferencing, Ontologies and Nanotechnology) project applies, extends, and combines Semantic Web technologies and image analysis techniques to develop a knowledge management system to optimize the design of fuel cells

    Systems biology driven software design for the research enterprise

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    <p>Abstract</p> <p>Background</p> <p>In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.</p> <p>Results</p> <p>We illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.</p> <p>Conclusion</p> <p>By using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.</p

    Semantic Restructuring of Natural Language Image Captions to Enhance Image Retrieval

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    WikiSensing: A collaborative sensor management system with trust assessment for big data

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    Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon. This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data. An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces
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