# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 8 Conclusions  
Process traces are a strategic source of information, which can be exploited to support different tasks, including the discovery of the underlying process model. Our work follows one of the main streams of research in process mining, as pointed out in [41], proposing the articulation of the discovery procedure into steps and involving the analysts in the discovery procedure itself [2]. Following such a general guideline, in our previous paper [3], we proposed an SIM (semantic interactive miner), an innovative process mining  
Figure 16: Versioning tree of the interactive session of work. For the sake of brevity and clarity, we represent as a cloud the sequence of nodes resulting from the application of the set of merging operations, as discussed in Section 3.4. Figure 15: Process model in Figure 13 resulting from the selective merging of _Monitoring_. approach supporting the possibility of involving analysts in the step-by-step selection of generalization operations, in order to merge parts of the model to achieve compactness and reduce redundancy. In this paper, we have substantially extended such an approach, to consider a closely related issue. In many medical domains, large bodies of knowledge are available and encoded a priori. In particular, in several cases, such knowledge consists of (or contains) a taxonomic description of the activities to be executed, considering the refinement (ISA) and partonomic (part-of) relationships between them. In these contexts, it seems to us a real \"waste\" of resources not to exploit such available knowledge in process discovery. Indeed, since the discovered process models can be very complex and difficult to interpret (i.e., \"spaghetti-like\" [1]), ISA and part-of knowledge can be precious for obtaining more abstract, compact, and easy-to-read models, in which unnecessary details are removed by moving to a higher level (in terms of the hierarchy provided by the pre-encoded knowledge base). The development of an approach exploiting available ISA and part-of knowledge in process model discovery, implemented and fully integrated into the open-source process mining framework ProM, is the main innovative contribution of this paper. Notably, the knowledge-based abstraction operations introduced in this paper are orthogonal (and complementary) to the merge ones we previously defined in [3]: merge operations generalize the process model by \"putting together\" recurrent patterns, but maintain the same level of detail in the description of the activities, while abstraction operations move to a higher hierarchical level. Given such an orthogonality, we have been able to integrate the new knowledge-based abstraction facilities in SIM through a modular approach, described in Section 6. The running example shows how such modularity facilitates the analyst in the discovery of a compact and easy-to-interpret process model. A fair comparison with other process mining algorithms is not possible, since existing approaches do not exploit semantic knowledge for interactive model abstraction. Nevertheless, in Appendix A, we propose an experimental comparison with four well-known mining algorithms embedded in ProM, based both on the calculation of some quantitative indicators and on a qualitative visual examination of the obtained models. The comparison shows the advantages of SIM, from the point of view of simplicity and readability. Additional quantitative indicators could be considered, as well. Indeed, in [3], we calculated a set of quantitative parameters, such as precision and replay fitness [4], for every model generated during the interactive session of work with the expert. This information was provided to the expert to support her/his evaluation of the current model, until s/he approved it as the final output of the process mining work.