Realizing the Hydrogen Economy through Semantic Web Technologies

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

Towards the Semantic Grid: A State of the Art Survey of Semantic Web Services and their Applicability to Collaborative Design, Engineering, and Procurement

Today, organizations within the engineering and manufacturing domains place as much emphasis on the management and flow of knowledge through a value chain as they do commodities that are more tangible in nature. For example, parts suppliers in the Canadian automotive sector are often asked to collaborate with auto manufacturers in designing and engineering their product, instead of simply producing and supplying it. Such fundamental changes in the overarching economics of this industry have led to a greater focus on collaboration, both in terms of communicating across geographic divides to design components, as well as new requirements to merge heterogeneous data stores in order to manage this distributed procurement process. Our work on this project centred on finding solutions to the above by surveying the state of the industry, as well as assessing the potential employability of related tools in the workplace. It was concluded that the Access Grid (a low-cost, open-source videoconferencing platform) held significant potential to facilitate the high-quality sharing of audiovisual material, while semantic technologies (the “semantic web” and “semantic web services”) represented a feasible solution to the issues of data integration. When combined, these technologies form the “semantic grid”, the focus of this paper. Overall, it is concluded that the past and present business success of this ICT in the information management sector may, with future work, link databases with the visualization interface to provide concurrent cost-benefit analyses

FUSION: A Knowledge Management System for Fuel Cell Optimization

Fuel cells are highly complex multi-component systems. Their efficiency depends on their internal nanostructure and the complex chemical and physical processes occurring across their internal interfaces. Significant advances in the accurate modeling of fuel cell components (electrodes, membrane and catalyst layers) can be achieved by improved analysis and assimilation of existing data and more systematic, controlled design and monitoring of tests and experiments. Within the FUSION project, we are employing the latest information technologies to provide a robust knowledge mining system for collaborative teams of fuel cell scientists

Multimedia Annotations on the Semantic Web

Multimedia in all forms (images, video, graphics, music, speech) is exploding on the Web. The content needs to be annotated and indexed to enable effective search and retrieval. However, recent standards and best practices for multimedia metadata don't provide semantically rich descriptions of multimedia content. On the other hand, the World Wide Web Consortium's (W3C's) Semantic Web effort has been making great progress in advancing techniques for annotating semantics of Web resources. To bridge this gap, a new W3C task force has been created to investigate multimedia annotations on the Semantic Web. This article examines the problems of semantically annotating multimedia and describes the integration of multimedia metadata with the Semantic Web. (Editor's note by John R. Smith)

Provenance explorer: Customized provenance views using semantic inferencing

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule-inference engine

Provenance Explorer: A Tool for Viewing Provenance Trails and Constructing Scientific Publication Packages

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule inference engine

Evaluating the application of semantic inferencing rules to image annotation

Semantic annotation of digital objects within large multimedia collections is a difficult and challenging task. We describe a method for semi-automatic annotation of images and apply it to and evaluate it on images of pancreatic cells. By comparing the performance of this approach in the pancreatic cell domain with previous results in the fuel cell domain, we aim to determine characteristics of a domain which indicate that the method will or will not work in that domain. We conclude by describing the types of images and domains in which we can expect satisfactory results with this approach. Copyright 2005 ACM

Generating eScience Workflows from Statistical Analysis of Prior Data

A number of workflow design tools have been developed specifically to enable easy graphical specification of workflows that ensure systematic scientific data capture and analysis and precise provenance information. We believe that an important component that is missing from these existing workflow specification and enactment systems is integration with tools that enable prior detailed analysis of the existing data - and in particular statistical analysis. By thoroughly analyzing the existing relevant datasets first, it is possible to determine precisely where the existing data is sparse or insufficient and what further experimentation is required. Introducing statistical analysis to experimental design will reduce duplication and costs associated with fruitless experimentation and maximize opportunities for scientific breakthroughs. In this paper we describe a workflow specification system that we have developed for a particular eScience application (fuel cell optimization). Experimental workflow instances are generated as a result of detailed statistical analysis and interactive exploration of the existing datasets. This is carried out through a graphical data exploration interface that integrates the widely-used open source statistical analysis software package, R, as a web service

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty