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Evaluating Scientific Hypotheses Using the SPARQL Inferencing Notation

  • Alison Callahan
  • Michel Dumontier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)

Abstract

Evaluating a hypothesis and its claims against experimental data is an essential scientific activity. However, this task is increasingly challenging given the ever growing volume of publications and data sets. Towards addressing this challenge, we previously developed HyQue, a system for hypothesis formulation and evaluation. HyQue uses domain-specific rulesets to evaluate hypotheses based on well understood scientific principles. However, because scientists may apply differing scientific premises when exploring a hypothesis, flexibility is required in both crafting and executing rulesets to evaluate hypotheses. Here, we report on an extension of HyQue that incorporates rules specified using the SPARQL Inferencing Notation (SPIN). Hypotheses, background knowledge, queries, results and now rulesets are represented and executed using Semantic Web technologies, enabling users to explicitly trace a hypothesis to its evaluation as Linked Data, including the data and rules used by HyQue. We demonstrate the use of HyQue to evaluate hypotheses concerning the yeast galactosegene system.

Keywords

hypothesis evaluation semantic web linked data SPARQL 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alison Callahan
    • 1
  • Michel Dumontier
    • 1
    • 2
    • 3
  1. 1.Department of BiologyCarleton UniversityOttawaCanada
  2. 2.School of Computer ScienceCarleton UniversityOttawaCanada
  3. 3.Institute of BiochemistryCarleton UniversityOttawaCanada

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