Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language

Yago Fontenla-Seco, Alberto Bugarín-Diz, Manuel Lama

In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. 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 use-case in the cardiology domain is presented, showing the potential of P2T for providing natural language explanations addressed to specialists.

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