Unleashing textual descriptions of business processes

Sànchez-Ferreres, Josep; Burattin, Andrea; Montali, Marco; Padró, Lluís; Quishpi, Luís

doi:10.1007/s10270-021-00886-x

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…A study argued that textual descriptions of processes in organizations were ubiquitous, 18 However, analyzing unstructured information directly is difficult. Therefore, a formal annotated text description (ATDP) method based on linear temporal logic representation was proposed to extract process models from business process description documents, and the ATDP was simulated and executed to generate event logs for process mining to obtain process models.…”

Section: Related Workmentioning

confidence: 99%

A‐PGRD: Attention‐based automatic business process model generation from RPA process description

Zhu,

Liu,

et al. 2023

Concurrency and Computation

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SummaryRobotic process automation (RPA), a tool driven by business processes as the kernel, continues to heat up in the business community. However, process‐centric RPA modeling lacks an effective means. To address this problem, we propose a method for automatic process acquisition using RPA process descriptions as input. Existing deep learning process generation methods cannot be applied at the phrase level and have low accuracy at the sentence level. The proposed neural network method is based on an attention mechanism for automatic business process model generation from RPA process descriptions (A‐PGRD). The approach analyzes easily accessible and unstructured natural language text documents, constructs a non‐autoregressive neural network with an attention mechanism to retrieve the business process hierarchy, and generates a tree‐like business process graph using unsupervised automation. Through K‐fold cross‐validation, the method achieves an accuracy of 41.7% on the manually collected open‐source RPA business process dataset. Compared with the previous method, the method improves the learning efficiency by 23%–27%. The obtained results can be applied to the RPA tool to better optimize the business process and thus help organizations gain an edge over their competition.

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Section: Related Workmentioning

confidence: 99%

A‐PGRD: Attention‐based automatic business process model generation from RPA process description

Zhu,

Liu,

et al. 2023

Concurrency and Computation

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“…Further works, such as the ones in [4] and [28], aim at supporting different forms of alignments between textual descriptions and business process models, and are therefore not considered here.…”

Section: Related Workmentioning

confidence: 99%

Leveraging pre-trained language models for conversational information seeking from text

Bellan¹,

Dragoni²,

Ghidini³

2022

Preprint

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Recent advances in Natural Language Processing, and in particular on the construction of very large pre-trained language representation models, is opening up new perspectives on the construction of conversational information seeking (CIS) systems. In this paper we investigate the usage of in-context learning and pre-trained language representation models to address the problem of information extraction from process description documents, in an incremental question and answering oriented fashion. In particular we investigate the usage of the native GPT-3 (Generative Pre-trained Transformer 3) model, together with two in-context learning customizations that inject conceptual definitions and a limited number of samples in a few shot-learning fashion. The results highlight the potential of the approach and the usefulness of the in-context learning customizations, which can substantially contribute to address the "training data challenge" of deep learning based NLP techniques the BPM field. It also highlight the challenge posed by control flow relations for which further training needs to be devised.

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“…A notable study [24] proposed an Integer Linear Programming (ILP) based approach to align textual process description with process model elements. In contrast, a recent study [25] has introduced a framework for the automatic extraction of annotated process descriptions and analysis with process model and the corresponding process event logs.…”

Section: Related Workmentioning

confidence: 99%

Augmenting Business Process Model Elements With End-User Feedback

Ahmed

Shahzad

2022

IEEE Access

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COVID-19 has imposed unprecedented restrictions on the society which has compelled the organizations to work ambidextrously. Consequently, the organizations need to go continuously monitor the performance of their business process and improve them. To facilitate that, this study has put-forth the idea of augmenting business process models with end-user feedback and proposed a machine learning based approach (AugProMo) to automatically identify correspondences between end-user feedback and elements of process models. In particular, we have generated three valuable resources, process models, feedback corpus and gold standard benchmark correspondences. Furthermore, 2880 experiments are performed to identify the most effective combination of word embeddings, feature vectors, data balancing and machine learning techniques. The study concludes that the proposed approach is effective for augmenting business process models with end-user feedback.

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Unleashing textual descriptions of business processes

Cited by 8 publications

References 42 publications

A‐PGRD: Attention‐based automatic business process model generation from RPA process description

A‐PGRD: Attention‐based automatic business process model generation from RPA process description

Leveraging pre-trained language models for conversational information seeking from text

Augmenting Business Process Model Elements With End-User Feedback

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