In this paper, we discuss a mechanism for transfer learning in the context of inductive process modeling. We begin by describing the dual role of knowledge as a source of model components and structural constraints. Next, we review the task of inductive process modeling and emphasize the effect of domain knowledge on the learning component. We then describe the performance and learning elements of the transfer task, define the form of that resulting knowledge, and introduce an evaluation methodology for the experiments. The reported results show the effect of cross-domain transfer within the larger field of ecology. We conclude by outlining future research in this area.