Evaluating scientific hypotheses using the SPARQL inferencing notation

Authors:
Alison Callahan;Michel Dumontier
Affiliations:
Department of Biology, Carleton University, Ottawa, Canada;Department of Biology, Carleton University, Ottawa, Canada,School of Computer Science, Carleton University, Ottawa, Canada,Institute of Biochemistry, Carleton University, Ottawa, Canada
Venue:
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Year:
2012

Citing 8
Cited 0

Design Methods for Scientific Hypothesis Formation and Their Application to Molecular Biology

Machine Learning
HinCyc: A Knowledge Base of the Complete Genome and Metabolic Pathways of H. influenzae

Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
HyBrow: a prototype system for computer-aided hypothesis evaluation

Bioinformatics
yOWL: An ontology-driven knowledge base for yeast biologists

Journal of Biomedical Informatics
Bio2RDF: Towards a mashup to build bioinformatics knowledge systems

Journal of Biomedical Informatics
Discovering drug–drug interactions

Bioinformatics
Reasoning with bio-ontologies

Bioinformatics
GenePath: a system for inference of genetic networks and proposal of genetic experiments

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

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.