Evaluating scientific hypotheses using the SPARQL Inferencing Notation

Presented at: 9th Extended Semantic Web Conference (ESWC2012)

by Alison Callahan, Michel Dumontier

Integrating experimental data and claims from the literature with hypotheses is an essential activity for the life scientist. Such a task is increasingly challenging given the ever growing volume of publications and data sets. Towards addressing this challenge, we previously developed HyQue, a prototype system for hypothesis formulation and evaluation. HyQue uses domain-specific rule sets to evaluate hypotheses based on well understood scientific principles. However, because scientists may use differing scientific premises when exploring a hypothesis, flexibility is required in both crafting and executing rule sets 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 rule sets are represented and executed using Semantic Web technologies, enabling users to explicitly trace a hypothesis to its evaluation, including the data and rules used by HyQue. We demonstrate the use of HyQue to evaluate hypotheses about the yeast galactose gene system.

Keywords: SPARQL, hypothesis evaluation, linked data, semantic web


Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/in-use-industrial/162


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