Reference models are a cost- and time-saving approach for the development of new models. As induc-tive strategies are capable of automatically deriving a potential reference process model from a col-lection of existing process models, they have gained attention in current research. A number of prom-ising approaches can be found in recent publications. However, all existing methods rely on graph-based similarity measures to identify commonalities between input models. Since behaviourally simi-lar process models can have different graphical structures, those approaches are unable to find cer-tain commonalities. To overcome these shortcomings, we propose a new approach to inductive refer-ence model development based on an execution-semantic similarity measure. Since a naïve solution to the intuitive idea does not yield productive results, the proposed approach is rather elaborate. By cap-turing the commonalities of the input models in a behavioural profile, we are able to derive a refer-ence model subsuming the input models’ semantics instead of their structure. In our contribution, this approach is outlined, implemented and evaluated in three different scenarios. As the evaluations show, it is capable of handling complex process models and overcome most restrictions that structural ap-proaches pose. Thus, it introduces a new level of flexibility and applicability to inductive reference modelling.