Many algorithms that derive gene regulatory networks from microarray gene expression data have been proposed in the literature. The performance of such an algorithm is often measured by how well a genetic network can recreate the gene expression data that the network was derived from. However, this kind of performance does not necessarily mean that the regulatory hypotheses in the network are biologically plausible. We therefore propose a Gene Ontology based method for assessing the biological plausibility of regulatory hypotheses at the gene product level using prior biological knowledge in the form of Gene Ontology annotation of gene products and regulatory pathway databases. Templates are designed to encode general knowledge, derived by generalizing from known interactions to typical properties of interacting gene product pairs. By matching regulatory hypotheses to templates, the plausible hypotheses can be separated from inplausible ones. In a cross-validation test we verify that the templates reliably identify interactions which have not been used in the template creation process, thereby confirming the generality of the approach. The method also proves useful when applied to an example network reconstruction problem, where a Bayesian approach is used to create hypothetical relations which are evaluated for biological plausibility. The cell cycle pathway and the MAPK signaling pathway for S. cerevisiae and H. sapiens are used in the experiments.