  Process Mining offers a powerful framework for uncovering, analyzing, and
optimizing real-world business processes. Petri nets provide a versatile means
of modeling process behavior. However, traditional methods often struggle to
effectively compare complex Petri nets, hindering their potential for process
enhancement. To address this challenge, we introduce PetriNet2Vec, an
unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets
into embedding vectors, facilitating the comparison, clustering, and
classification of process models. We validated our approach using the PDC
Dataset, comprising 96 diverse Petri net models. The results demonstrate that
PetriNet2Vec effectively captures the structural properties of process models,
enabling accurate process classification and efficient process retrieval.
Specifically, our findings highlight the utility of the learned embeddings in
two key downstream tasks: process classification and process retrieval. In
process classification, the embeddings allowed for accurate categorization of
process models based on their structural properties. In process retrieval, the
embeddings enabled efficient retrieval of similar process models using cosine
distance. These results demonstrate the potential of PetriNet2Vec to
significantly enhance process mining capabilities.
