Evaluating Scientific Hypotheses Using the SPARQL Inferencing Notation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Alison Callahan , Michel Dumontier

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. More... »

PAGES

647-658

Book

TITLE

The Semantic Web: Research and Applications

ISBN

978-3-642-30283-1
978-3-642-30284-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-30284-8_50

DOI

http://dx.doi.org/10.1007/978-3-642-30284-8_50

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045437595


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