# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
###### Abstract  
The traces of process executions are a strategic source of information, from which a model of the process can be mined. In our recent work, we have proposed SIM (semantic interactive miner), an innovative process mining tool to discover the process model incrementally: it supports the interaction with domain experts, who can selectively merge parts of the model to achieve compactness, generalization, and reduced redundancy. We now propose a substantial extension of SIM, making it able to exploit (both automatically and interactively) pre-encoded taxonomic knowledge about the refinement (ISA relations) and composition (part-of relations) of process activities, as is available in many domains. The extended approach allows analysts to move from a process description where activities are reported at the ground level to more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the \"macro-activities\" subsuming them or constituted by them. An experimental evaluation based on a real-world setting (stroke management) illustrates the advantages of our approach. knowledge-based process model discovery; process model abstraction +
Footnote †: journal: _Future Internet_  
1. The possibility of giving domain experts/analysts an active role in the discovery of the process model. 2. The possibility of adopting, besides \"syntactic\" forms of abstraction, also \"semantic\" (our terminology (specifically, we term \"syntactic\" all those forms of abstraction that are independent of the specific activities (but they consider, e.g., their number and/or their order); on the other hand, we term \"semantic\" those abstractions that depend on the specific activities (so that they require some form of--explicit or implicit--semantic knowledge about the activities in the domain))) ones. Indeed, in several contexts in which process mining is applied (e.g., in medicine), a lot of domain knowledge is available, additionally, domain experts and analysts can provide a remarkable contribution to model discovery. Following the direction started by [2], we have recently proposed an _SIM_ (semantic interactive miner) [3], to give analysts a way to flexibly interact with the process model construction (instead of just choosing between models autonomously built by the system), directly exploiting their domain knowledge and taking advantage of proper querying facilities. We started from a non-generalized model (i.e., a model with _precision_ = 1 and _replay fitness_ = 1 [4]), and let the analysts drive the generalization through multiple \"semantic\" merge steps, which allow for the merging of different occurrences of patterns of activities (detected through the querying mechanism provided by SIM) in the model. At each step, the analysts may analyze the current model quantitatively and/or qualitatively (using SIM's query language) and approve it or move to a further merge, instead of backtrack to some previous version. Notably, SIM supports analysts in a selective application of the merging tools (differently from the \"standard syntactic\" abstraction steps, which are uniformly applied for the whole model--consider, e.g., [2]). In this paper, we extend SIM to support the exploitation (either automatic or driven by analysts) of pre-defined and pre-encoded domain knowledge. Specifically, in many medical domains, it is possible to rely on a hierarchical representation of the activities, where _ISA_ relationships support the definition of activities at different levels of detail, and _Part-of_ relationships refine the description of activities by specifying the sub-activities composing them. We propose a semantic-based _abstraction_ technique that allows analysts to move from a process description where activities are reported at the ground level to a more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the \"macro-activities\" subsuming them (_ISA_ relationships) or composed by them (_part-of_ relationships).