Peter Edwards

Publications (5)0 Total impact

• Source

  Available from: Edoardo Pignotti

  Chapter: Enhancing Workflow with a Semantic Description of Scientific Intent

  Edoardo Pignotti, Peter Edwards, Alun Preece, Nick Gotts, Gary Polhill
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  ABSTRACT: In the e-Science context, workflow technologies provide a problem-solving environment for researchers by facilitating the creation and execution of experiments from a pool of available services. In this paper we will show how Semantic Web technologies can be used to overcome a limitation of current workflow languages by capturing experimental constraints and goals, which we term scientist’s intent. We propose an ontology driven framework for capturing such intent based on workflow metadata combined with SWRL rules. Through the use of an example we will present the key benefits of the proposed framework in terms of enriching workflow output, assisting workflow execution and provenance support. We conclude with a discussion of the issues arising from application of this approach to the domain of social simulation.
  05/2008: pages 644-658;
• Source

  Available from: users.cs.cf.ac.uk

  Chapter: Instance Based Clustering of Semantic Web Resources

  Gunnar AAstrand Grimnes, Peter Edwards, Alun Preece
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  ABSTRACT: The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.
  05/2008: pages 303-317;
• Article: An Empirical Investigation of Learning from the Semantic Web

  Peter Edwards, Gunnar Aastr, Alun Preece
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  ABSTRACT: The Semantic Web is a vision of a machine readable Web of resources, interlinked and connected through meta-data with common ontologies. In this paper we explore the impact such a Semantic Web would have on Machine Learning algorithms used for user profiling and personalisation. Our hypothesis is that learning from the Semantic Web should outperform traditional learning from today 's World Wide Web for both performance and accuracy. In this paper we present results obtained with two different datasets marked-up with semantic meta-data; using these we have investigated different instance representations and various learning techniques. Our initial results with the Nave Bayes and K-NN algorithms were disappointing, leading us to examine the use of the Progol algorithm.
  08/2002;
• Source

  Available from: aclweb.org

  Article: Evaluating an ontology-driven WYSIWYM interface

  Feikje Hielkema, Chris Mellish, Peter Edwards
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  ABSTRACT: This paper describes an evaluation study of an ontology-driven WYSIWYM interface for metadata creation. Although the results are encouraging, they are not as positive as those of a similar tool developed for the medical domain. We believe this may be due, not to the WYSIWYM interface, but to the complex-ity of the underlying ontologies and the fact that subjects were unfamiliar with them. We discuss the ways in which ontology develop-ment might be influenced by issues stemming from using an NLG approach for user access to data, and the effect these factors have on general usability.
• Source

  Available from: Edoardo Pignotti

  Article: Using the Grid to Support Evidence-Based Policy Assessment in Social Science

  Peter Edwards, Alison Chorley, Feikje Hielkema, Edoardo Pignotti, Alun Preece, Chris Mellish, John Farrington
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  ABSTRACT: The PolicyGrid project is exploring the role of Semantic Grid technologies to support eScience for the social sciences, with a particular emphasis on tools to facilitate evidence-based policy making. In this paper we highlight some of the key challenges facing developers of semantic infrastructure and tools for social science researchers. We outline a framework for evidence management, discuss issues surrounding creation and presentation of metadata, describe a Web-based service which utilises a natural language interface to facilitate creation of RDF, and present a desktop qualitative analysis tool which is integrated with our evidence framework.