PETGoldstandardBaselinesVisualizers 1.0.2

Visualizers of the PET dataset goldstandard data and baselines prediction

PET Dataset Goldstandard and Baselines predictions visualizers

This package provides two graphical interfaces to visualize the PET dataset goldstandard annotations and the baselines predictions.

created by Patrizio Bellan

The PET Dataset

Abstract. Process extraction from text is an important task of process discovery, for which various approaches have been developed in recent years. However, in contrast to other information extraction tasks, there is a lack of gold-standard corpora of business process descriptions that are carefully annotated with all the entities and relationships of interest. Due to this, it is currently hard to compare the results obtained by extraction approaches in an objective manner, whereas the lack of annotated texts also prevents the application of data-driven information extraction methodologies, typical of the natural language processing field. Therefore, to bridge this gap, we present the PET dataset, a first corpus of business process descriptions annotated with activities, gateways, actors, and flow information. We present our new resource, including a variety of baselines to benchmark the difficulty and challenges of business process extraction from text. PET can be accessed via huggingface.co/datasets/patriziobellan/PET

PET Dataset Data

PET dataset repository

(Inception) Annotation Schema

Guidelines

Baseline Predictor Agreement Interface

This visualizer shows the agreement between the baseline predictors and the goldstandard annotations.

from Visualizers.BaselinePredictorAgreementInterface import Show as agreements
agreements()

Baselines Comparison LineView

This visualizer provides a GUI to compare the goldstandard process element annotations and the Baseline 1 ones.

from Visualizers.BaselinesComparisonLineView import Show as lineview
lineview()

Cite the PET dataset

PET Dataset

@inproceedings{DBLP:conf/bpm/BellanADGP22,
  author       = {Patrizio Bellan and
                  Han van der Aa and
                  Mauro Dragoni and
                  Chiara Ghidini and
                  Simone Paolo Ponzetto},
  editor       = {Cristina Cabanillas and
                  Niels Frederik Garmann{-}Johnsen and
                  Agnes Koschmider},
  title        = {{PET:} An Annotated Dataset for Process Extraction from Natural Language
                  Text Tasks},
  booktitle    = {Business Process Management Workshops - {BPM} 2022 International Workshops,
                  M{\"{u}}nster, Germany, September 11-16, 2022, Revised Selected
                  Papers},
  series       = {Lecture Notes in Business Information Processing},
  volume       = {460},
  pages        = {315--321},
  publisher    = {Springer},
  year         = {2022},
  url          = {https://doi.org/10.1007/978-3-031-25383-6\_23},
  doi          = {10.1007/978-3-031-25383-6\_23},
  timestamp    = {Tue, 14 Feb 2023 09:47:10 +0100},
  biburl       = {https://dblp.org/rec/conf/bpm/BellanADGP22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Guidelines

@inproceedings{DBLP:conf/aiia/BellanGDPA22,
  author       = {Patrizio Bellan and
                  Chiara Ghidini and
                  Mauro Dragoni and
                  Simone Paolo Ponzetto and
                  Han van der Aa},
  editor       = {Debora Nozza and
                  Lucia C. Passaro and
                  Marco Polignano},
  title        = {Process Extraction from Natural Language Text: the {PET} Dataset and
                  Annotation Guidelines},
  booktitle    = {Proceedings of the Sixth Workshop on Natural Language for Artificial
                  Intelligence {(NL4AI} 2022) co-located with 21th International Conference
                  of the Italian Association for Artificial Intelligence (AI*IA 2022),
                  Udine, November 30th, 2022},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3287},
  pages        = {177--191},
  publisher    = {CEUR-WS.org},
  year         = {2022},
  url          = {https://ceur-ws.org/Vol-3287/paper18.pdf},
  timestamp    = {Fri, 10 Mar 2023 16:23:01 +0100},
  biburl       = {https://dblp.org/rec/conf/aiia/BellanGDPA22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}