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.

翻译：本文提出一系列模糊时间原形式，用于自动生成过程的定量与定性自然语言描述。该模型整合了过程与属性的时间及因果信息，量化属性在过程生命周期内的时序特征，并还原事件间的因果关系与时间距离等要素。通过将过程挖掘技术与模糊集融入常规数据-文本架构，本文框架能够从过程中提取相关定量时间及结构信息，并以包含不确定术语的自然语言进行描述。最后以心脏病学领域的真实应用案例展示该模型为领域专家提供自然语言解释的潜力。