Fuzzy Temporal Protoforms for the Quantitative Description of Processes
  in Natural Language
Yago Fontenla-Seco Alberto Bugarín-Diz Manuel Lama
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.