# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 4 A Model for the Domain Knowledge  
Many approaches to process model discovery, including our SIM system [3], only rely on a list containing the possible activities that may appear in the traces, and, thus, in the process model. While such approaches are the only possible ones in contexts where no a priori knowledge is available, in other contexts, wide bodies of knowledge exist about the processes operating in the domain and about the relationships between the activities they involve. In many cases, such knowledge is organized into knowledge bases called _ontologies_, which may include descriptions of the activities, in terms of their properties (e.g., resources, goals, effects, and side-effects) and of the relationships between them (e.g., ISA,  
Figure 3: Process model after the merge operations. Figure 2: The results of the example search. part-of, and causes). This is the case of the medical domain, for instance, where well-known and widely used ontologies are available (e.g., SNOMED [38], ATC [39]), which are often based on widely adopted AI frameworks, such as OWL [40]. When such ontological knowledge is available, it is worth using it within process mining. The main goal of this paper is to show how (a part of) such knowledge can be integrated into process model discovery to support experts in the identification of a suitable process model with the desired level of abstraction. In particular, given their importance and generality, in this paper, we focus on two forms of knowledge: (i) _ISA_ relationships and (ii) _part-of_ relationships. Specifically:  
* **ISA** relationships are (subtype-supertype) _subsumption_ relations between classes (of entities; in our case, of activities): a class of activities \(A\) is a superclass of \(B\) when \(A\)'s specification implies \(B\)'s specification. For instance, **in the knowledge base in Figure 4, intravenous thrombolysis (_r-TPA Proc_), intra-arterial thrombolysis (_TPAia Proc_), and pharmacological therapy (_PH Proc_) are subclasses of _Treatment_. * **Part-of** relationships are _partonomic_ (i.e., part-whole) relations between classes; in the case of activities, they relate a class \(A\) with the classes \(C_{1}\),...,\(C_{n}\) composing it, modeling the decomposition of composite activities into the sub-activities composing them. For instance, **in the knowledge base in Figure 4**, _Stroke_Management_ is composed of four (sub-)activities: _Evaluation_LI_, _Treatment_, _Monitoring_LI_, and _Rehab_LI (rehabilitation)_. Notably, the ISA and part-of relations provide a hierarchical representation of activities, in the form of a direct acyclic graph (DAG), that we call an _ontological knowledge base_ (KB). For the sake of simplicity (and with no loss of generality), we suppose that the KB hierarchy has a unique (\"catch-all\") _starting node_. Notably, the choice of a DAG (instead of a tree) allows us to represent the hierarchical knowledge more compactly, admitting, e.g., that a given node (activity) is part of more than one node (activity). In the rest of the paper, we adopt the following terminology. **Terminology**. We term _\"abstraction levels\"_ (_levels_, for short) as the levels in the hierarchy. We say that the _starting node_ of the KB hierarchy is at **level zero**, its direct descendants (termed _\"direct sons\"_) are at **level one**, and so on. We partition the nodes in the hierarchy into three types:  
* **\"ISA nodes\"**, i.e., nodes \(N\), whose sons in the KB hierarchy are in an ISA relationship with \(N\);  
Figure 4: The KB describing the main stroke management procedures in acute phase. * **\"Part-Of nodes\"**, i.e., nodes \(N\), whose sons in the KB hierarchy are in a part-of relationship with \(N\);
* **\"ground nodes\"**, i.e., nodes that have no sons in the KB hierarchy (representing the ground non-decomposable activities appearing in the input traces in the log). Note that a specific node can only appear at a given level of the KB (since it represents an activity expressed at a precise level of abstraction).