Browsing by Author "Puleston, C."

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    Applying the Semantic Web to Manage Knowledge on the Grid
    (e-science, 2004-08-31) Tao, F.; Cox, S.; Chen, Liming; Shadbolt, N.; Xu, F.; Puleston, C.; Goble, C.
    Geodise [2] uses a toolbox of Grid enabled Matlab functions as building blocks on which higher-level problem solving workflows can be built. The aim is to help domain engineers utilize the Grid and engineering design search packages to yield optimized designs more efficiently. In order to capture the knowledge needed to describe the functions & workflows so that they may be best reused by other less experienced engineers we have developed a layered semantic infrastructure. A generic knowledge development and management environment (OntoView) that is used to develop an ontology encapsulating the semantics of the functions and workflows, and that underpins the domain specific components. These include: an annotation mechanism used to associate concepts with functions (Function Annotator); a semantic retrieval mechanism and GUI that allows engineers to locate suitable functions based on a list of ontology-driven searching criteria; and a GUI-based function advisor that uses the functions’ semantic information in order to help function configuration and recommend semantically compatible candidates for function assembly and workflow composition (Domain Script Editor and Workflow Construction Advisor). This paper describes this infrastructure, which we plan to extend to include the semantic reuse of workflows as well as functions.
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    Empowering Resource Providers to Build the Semantic Grid
    (IEEE, 2004-04-04) Chen, Liming; Cox, S.J.; Tao, F.; Shadbolt, N.; Goble, C.; Puleston, C.
    The future success of Grid-enabled e-Science depends on the availability of semantic/knowledge-rich resources on the Grid, i.e., the so-called semantic Grid. This requires not only novel knowledge modelling and representation formalisms but also a shift of knowledge acquisition and population from a limited number of specialised knowledge engineers to resource providers. To this end we have developed a lightweight ontology-enabled tool, "Function Annotator", to support resource providers in capturing and publishing resource semantics and knowledge. Function Annotator takes a different line to most knowledge acquisition tools in that it is designed for use by resource providers, probably in the absence of a knowledge engineer. Its aim is to facilitate large scale knowledge population on the Grid. Function Annotator is built on the state-of-the-art of semantic web technologies, such as ontologies, OWL, instance store and DL-based reasoning, thus ensuring flexibility and scalability on the Grid. This paper describes the tool's role in a Grid-oriented resource lifecycle, its underlying technologies and implementation. It also illustrates the usage of the tool in the context of engineering design search and optimisation.
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    Exploiting Semantics for e-Science on the Semantic Grid
    (2003-10-13) Chen, Liming; Shadbolt, N.; Tao, F.; Puleston, C.; Goble, C.; Cox, S.J.
    In this paper we address the problem of exploiting semantics for e-Science [1] in the emerging future e-Science infrastructure - the Semantic Grid [2]. The discussion is taken in the context of Grid enabled optimisation and design search in engineering (“Geodise” project) [3]. In our work we have developed a semantics based Grid-enabled computing architecture for Geodise. The architecture has incorporated a service-oriented distributed knowledge management framework for providing semantic and knowledge support. It uses ontologies as the conceptual backbone for information level and knowledge-level computation. Geodise resources including computational codes, capabilities and knowledge are semantically enriched using ontologies through annotations, thus facilitating seamless access, flexible interoperation and resource sharing on the Grid. We describe ontological engineering work and various approaches to semantic enrichment in Geodise. The semantically enriched content together with the Semantic Grid paradigm have been used as the foundation for the development of an ontology-enabled Geodise problem solving environment prototype (PSE). We have partially implemented the workflow construction environment in the Geodise PSE in which semantics is exploited to describe, discover and compose engineering computation resources for engineering problem-solving.
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    Managing Semantic Metadata for the Semantic Grid
    (IEEE, 2004-09-20) Chen, Liming; Shadbolt, N.; Goble, C.; Tao, F.; Cox, S.J.; Puleston, C.
    Research on the Semantic Web and Web/Grid resource description, discovery and composition is booming but there is currently little effort on a systematic and integrated approach to the management of resources’ Semantic Metadata (SMD), nor on key tools that add, store and reuse SMD. In this paper we propose a generic framework for managing resource SMD, in which ontologies are used for metadata modeling and the Web Ontology Language (OWL) for semantic representation. Generated resource SMD are archived in a knowledge repository enhanced with Description Logic (DL) based reasoning capability. A raft of tools, mechanisms and APIs are developed to support SMD management lifecycle, including metadata generation, semantic annotation, knowledge storage and semantic reuse. Both the framework and its supporting technologies have been applied to a large existing e-Science project, which has produced a working resource management prototype. While SMD can be exploited in many ways with regards to resource discovery, provenance and trust, we illustrate their usage through a knowledge advisor that assists resource assembly and configuration in the context of engineering design search and optimisation
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    Semantic Support for Grid-Enabled Design Search in Engineering
    (2003-10-05) Tao, F.; Chen, Liming; Cox, S.J.; Shadbolt, N.; Puleston, C.; Goble, C.
    Semantic Web technologies are evolving the Grid towards the Semantic Grid [2] to yield an intelligent grid which allows seamless process automation, easy knowledge reuse and collaboration within a community of practice. We discuss our endeavours in this direction in the context of Grid enabled optimisation and design search in engineering (“Geodise” project) [3]. In our work we have developed a semantics-based Grid-enabled computing architecture for Geodise. The architecture incorporates a service-oriented distributed knowledge management framework for providing various semantic and knowledge support. It uses ontologies as the conceptual backbone for information-level and knowledge-level computation. We also describe ontological engineering work and a service-oriented approach to ontology deployment. We present several application examples that show the benefit of semantic support in Geodise.
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    Semantics-assisted Problem Solving on the Semantic Grid
    (Wiley, 2005-03-23) Chen, Liming; Shadbolt, N.; Goble, C.; Tao, F.; Puleston, C.
    In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e-Science. The framework uses ontology as the conceptual backbone and adopts the service-oriented computing paradigm for information- and knowledge-level computation. We further present a semantics-based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e-Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology-enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.
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    Towards a Knowledge-based Approach to Semantic Service Composition
    (Springer, 2003-10-20) Chen, Liming; Shadbolt, N.; Goble, C.; Tao, F.; Cox, S.J.; Puleston, C.; Smart, P.
    The successful application of Grid and Web Service technologies to real-world problems, such as e-Science [1], requires not only the development of a common vocabulary and meta-data framework as the basis for inter-agent communication and service integration but also the access and use of a rich repository of domain-specific knowledge for problem solving. Both requirements are met by the respective outcomes of ontological and knowledge engineering initiatives. In this paper we discuss a novel, knowledge-based approach to resource synthesis (service composition), which draws on the functionality of semantic web services to represent and expose available resources. The approach we use exploits domain knowledge to guide the service composition process and provide advice on service selection and instantiation. The approach has been implemented in a prototype workflow construction environment that supports the runtime recommendation of a service solution, service discovery via semantic service descriptions, and knowledge-based configuration of selected services. The use of knowledge provides a basis for full automation of service composition via conventional planning algorithms. Workflows produced by this system can be executed through a domain-specific direct mapping mechanism or via a more fluid approach such as WSDL-based service grounding. The approach and prototype have been used to demonstrate practical benefits in the context of the GEODISE initiative [2].
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    Towards the Semantic Grid: Enriching Content for Management and Reuse
    (e-science, 2003-09-01) Tao, F.; Cox, S.J.; Chen, Liming; Shadbolt, N.; Xu, F.; Puleston, C.; Goble, C.; Song, W
    Knowledge and Semantic Web technologies are evolving the Grid towards the Semantic Grid [18] to facilitate knowledge reuse and collaboration within a community of practice. In the Geodise project we are exploring the application of a range of knowledge and Semantic Web technologies to assist users in solving complex problems in Engineering Design Search and Optimization (EDSO), in particular enabling semantically enriched resource sharing and reuse.