Process-To-Text: a framework for the quantitative description of processes in natural language
In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of business processes in natural language. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms and natural language generation for building the explanations. A real usecase in the medical domain is presented, showing the potential of P2T for providing natural language explanations addressed to cardiology specialists.
keywords: Process mining, Natural Language Generation, AutoAI, Explainable AI