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 have developed HyQue, a system for hypothesis formulation and evaluation. HyQue uses domain-specific rule sets to evaluate hypotheses based on well understood scientific principles.
A unique feature of HyQue is that input hypotheses, the rules and data used to evaluate hypotheses, and the output evaluations are represented using Semantic Web standard languages (RDF and OWL). This allows users to query, aggregate and assert new facts about all aspects of the HyQue system.
Callahan, Alison & Michel Dumontier. 2012. Evaluating scientific hypotheses using the SPARQL Inferencing Notation. In Simperl, Elena, Philipp Cimiano, Axel Polleres, Oscar Corcho, Valentina Presutti (Eds.) The Semantic Web: Research and Applications. Lecture Notes in Computer Science Volume 7295, pp. 647-658.
Callahan, Alison, Michel Dumontier & Nigam H. Shah. 2011. HyQue: Evaluating hypotheses using Semantic Web technologies. Journal of Biomedical Semantics 2(Suppl 2): S3.