Automated Abstraction of Operation Processes from Unstructured Text for Simulation Modeling
This work addresses the need to reduce interpretation load and ensure reliability for simulation modelers, though it appears incremental as it builds on existing extraction techniques.
The paper tackles the problem of manually abstracting operation processes from unstructured text for simulation modeling by proposing an automated methodology using rule-based information extraction to create graphical representations, demonstrating feasibility on an earthmoving operation case.
Abstraction of operation processes is a fundamental step for simulation modeling. To reliably abstract an operation process, modelers rely on text information to study and understand details of operations. Aiming at reducing modelers' interpretation load and ensuring the reliability of the abstracted information, this research proposes a systematic methodology to automate the abstraction of operation processes. The methodology applies rule-based information extraction to automatically extract operation process-related information from unstructured text and creates graphical representations of operation processes using the extracted information. To demonstrate the applicability and feasibility of the proposed methodology, a text description of an earthmoving operation is used to create its corresponding graphical representation. Overall, this research enhances the state-of-the-art simulation modeling through achieving automated abstraction of operation processes, which largely reduces modelers' interpretation load and ensures the reliability of the abstracted operation processes.