Evaluating scientific hypotheses using the SPARQL inferencing notation

Alison Callahan, M. Dumontier

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


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.
Original languageEnglish
Title of host publicationThe Semantic Web: Research and Applications
Subtitle of host publication9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27-31, 2012. Proceedings
Number of pages12
ISBN (Electronic)9783642302848
ISBN (Print)9783642302831
Publication statusPublished - 2012
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science

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