Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text
This work addresses the time-consuming design of formal business process models for companies, offering an incremental improvement over existing methods by making the pipeline data-driven and handling linguistic references.
The paper tackles the problem of automating business process model generation from natural language text by addressing two key subtasks: extracting process-relevant information and creating models, with a focus on moving from rule-based to data-driven methods and resolving linguistic references. It demonstrates competitiveness by eliminating feature engineering overhead and enabling adaptation to new domains, and extends the PET dataset to include linguistic reference information for improved model quality.
Process-aware information systems offer extensive advantages to companies, facilitating planning, operations, and optimization of day-to-day business activities. However, the time-consuming but required step of designing formal business process models often hampers the potential of these systems. To overcome this challenge, automated generation of business process models from natural language text has emerged as a promising approach to expedite this step. Generally two crucial subtasks have to be solved: extracting process-relevant information from natural language and creating the actual model. Approaches towards the first subtask are rule based methods, highly optimized for specific domains, but hard to adapt to related applications. To solve this issue, we present an extension to an existing pipeline, to make it entirely data driven. We demonstrate the competitiveness of our improved pipeline, which not only eliminates the substantial overhead associated with feature engineering and rule definition, but also enables adaptation to different datasets, entity and relation types, and new domains. Additionally, the largest available dataset (PET) for the first subtask, contains no information about linguistic references between mentions of entities in the process description. Yet, the resolution of these mentions into a single visual element is essential for high quality process models. We propose an extension to the PET dataset that incorporates information about linguistic references and a corresponding method for resolving them. Finally, we provide a detailed analysis of the inherent challenges in the dataset at hand.