Fuzzy Temporal Protoforms for the Quantitative Description of Processes in Natural Language
This work addresses the need for natural language explanations of processes for domain experts, but it appears incremental as it builds on existing Data-to-Text architectures with fuzzy sets.
The paper tackles the problem of automatically generating quantitative and qualitative natural language descriptions of processes by proposing fuzzy temporal protoforms, integrating process mining and fuzzy sets to extract temporal and structural information. It demonstrates the model's potential in a cardiology use-case for providing explanations to domain experts.
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.