In this paper, we propose a series of fuzzy temporal
protoforms in the framework of the automatic generation of
quantitative and qualitative natural language descriptions of
processes. The model includes temporal and causal information
from processes and attributes, quantifies attributes in time during
the process life-span and recalls causal relations and temporal
distances between events, among other features. Through integrating
process mining techniques and fuzzy sets within the
usual Data-to-Text architecture, our framework is able to extract
relevant quantitative temporal as well as structural information
from a process and describe it in natural language involving
uncertain terms. A real use-case in the cardiology domain is
presented, showing the potential of our model for providing
natural language explanations addressed to domain experts.
Keywords: Process mining, Protoforms, Linguistic Description of Data, Natural Language Generation