🤖 AI Summary
This work addresses the challenge of automatically achieving deep semantic understanding of curve-based charts in technical documentation, which often lack structured semantic representations. To this end, the authors propose Kyrtos, a novel method that first segments curves by clustering inflection points and analyzing behavioral features such as direction and trend. It then constructs an attributed graph and generates corresponding natural language descriptions, ultimately mapping the representation end-to-end into a stochastic Petri net (SPN) to capture the chart’s internal functional logic. This study presents the first approach to jointly represent curve structures through attributed graphs and natural language while enabling direct conversion to SPNs. Experimental results demonstrate that Kyrtos accurately reconstructs both structural and semantic relationships in multi-function curve charts, significantly advancing the deep semantic understanding of technical diagrams.