Configuring Online Problem-Solving Resources with the Internet Reasoning Service

Crubézy, Monica; Lu, Wenjin; Motta, Enrico; Musen, Mark A.

doi:10.1007/978-0-387-35602-0_9

Cited by 7 publications

(5 citation statements)

References 6 publications

(10 reference statements)

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“…Technical support in the area of reuse tends to be focused on the type of product being reused. At one end of the spectrum we have reuse of ontologies in tools such as Protégé [Schreiber00b], at the other there are tools to facilitate the reuse of complete problem solving architectures [Motta99,Fensel99,Crubézy02]. Obstacles to reuse include the very real possibility that it is sometimes easier to reconstruct the knowledge fragment than hunt for it.…”

Section: The Knowledge Lifecyclementioning

confidence: 99%

The Semantic Grid: A Future e‐Science Infrastructure

2003

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e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice-aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid.

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Section: The Knowledge Lifecyclementioning

confidence: 99%

The Semantic Grid: A Future e‐Science Infrastructure

2003

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“…There has been some work on using PSMs in developing Semantic Web applications. The IBROW 3 project (Benjamins et al, 1998), for example, spawned the Internet Reasoning Service (IRS), which aimed to provide a framework for component discovery and composition on the Web (Crubézy et al, 2003). The ultimate goal of the IRS project was to make PSMs available as Web resources.…”

Section: Discussionmentioning

confidence: 99%

“…Over the past 6 years, we have produced two versions of BioSTORM, each with different goals. The first system focused on analyzing a variety of data sources, which were described with a rich domain ontology (Crubézy et al, 2003). The system was built with relatively simple PSMs.…”

Section: Introductionmentioning

confidence: 99%

Software-engineering challenges of building and deploying reusable problem solvers

O’Connor

Nyulas

et al. 2009

AIEDAM

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Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task–method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

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“…In part, the advent of the Model-Driven Architecture (MDA) approach promoted by the Object Management Group has made this kind of procedural modelling commonplace [23]. More importantly, the popularization of service-oriented architectures and Web services in the software-engineering community has provided the hooks that make it straightforward for system builders to translate their abstract models of problem solving into implemented services that can be invoked by software applications in standardized ways over the Internet [24].…”

Section: Caring About the Contentmentioning

confidence: 99%

Biomedical Informatics – A Confluence of Disciplines?

Hasman¹,

Ammenwerth²,

Dickhaus

et al. 2011

Methods Inf Med

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SummaryBackground: Biomedical informatics is a broad discipline that borrows many methods and techniques from other disciplines. Objective: To reflect a) on the character of biomedical informatics and to determine whether it is multi-disciplinary or inter-disciplinary; b) on the question whether biomedical informatics is more than the sum of its supporting disciplines and c) on the position of biomedical informatics with respect to related disciplines. Method: Inviting an international group of experts in biomedical informatics and related disciplines on the occasion of the 50th anniversary of Methods of Information in Medicine to present their viewpoints. Results and Conclusions: This paper contains the reflections of a number of the invited experts on the character of biomedical informatics. Most of the authors agree that biomedical informatics is an interdisciplinary

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Configuring Online Problem-Solving Resources with the Internet Reasoning Service

Cited by 7 publications

References 6 publications

The Semantic Grid: A Future e‐Science Infrastructure

The Semantic Grid: A Future e‐Science Infrastructure

Software-engineering challenges of building and deploying reusable problem solvers

Biomedical Informatics – A Confluence of Disciplines?

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