Motivation: Genetic and pharmacological perturbations are powerful systems biology tools to study cellular signal transduction pathways.Here, we report a framework to unify knowledge mining of gene functions and data mining of systematic perturbation data to identify modules of genes that likely constitute signal transduction pathways.Results: Our framework consists of three steps: 1) applying ontology-guided knowledge mining approaches to identify functionally coherent modules among the genes that respond to systematic perturbations ; 2) employing a graph algorithm to identify perturbation instances that are densely connected to the genes in a responding module, as a means to reveal the members of a potential signaling pathway ; 3) organizing perturbation instances in a manner that genes carrying common signals are grouped as a signal component, and the relationships among different signal components are further revealed as a network.Applying our approach to a compendium of yeast systematic perturbation data, we have successfully identified perturbation modules, with some corresponding to well-known signal transduction pathways and some leading to new hypotheses.Further grouping perturbed genes into signaling components revealed the organization of cellular signaling systems.Our results have led to new hypotheses regarding yeast cellular signaling systems: some are supported by existing publications and some need further validation.In conclusion, we have demonstrated that our framework enables conceptualization of experimental results, which enhances the capability of identifying signal transduction pathways from systematic perturbation data and leads to new directions of knowledge acquisition and representation .