One of the central tenets of experimental science is that the formulation and testing of hypotheses will lead to an improved understanding of (biological) systems that cannot be directly observed due to their complexity and size. Similarly, key to the success of e-Science is the ability to evaluate complex knowledge and expert-composed hypotheses by checking their validity against existing experimental data. HyQue is a Semantic Web-based tool for querying and evaluating scientific hypotheses that is motivated by its predecessor, the hypothesis browser HyBrow. HyQue features a flexible knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated according to experimental and literature-sourced evidence through a unique combination of SPARQL queries and query evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. Unlike HyBrow, this system is capable of executing queries where participating entities or event types are unspecified, thus opening the door to a significantly wider range of hypothesis support. HyQue has been used to evaluate hypotheses with varying levels of detail in the context of the galactose gene network in Saccharomyces cerevisiae. Future work with HyQue will include integration with an open information extraction tool to capture domain knowledge from scientific articles.