Annotation and Extraction of Industrial Procedural Knowledge from Textual Documents-Reference-Cited by-同舟云学术

Annotation and Extraction of Industrial Procedural Knowledge from Textual Documents

Published:2023-12-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th Knowledge Capture Conference 2023
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Author:

Rula Anisa¹^ORCID,Calegari Gloria Re²^ORCID,Azzini Antonia²^ORCID,Baroni Ilaria²^ORCID,Celino Irene²^ORCID

Affiliation:

1. Information Engineering, University of Brescia, Italy

2. Cefriel - Politecnico di Milano, Italy

Funder

It is also partially funded by the European Union (PERKS)

was partially supported by the K-HUB (Manufacturing Knowledge Hub) project, co-funded by EIT Manufacturing

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3587259.3627570

Reference13 articles.

1. Patrizio Bellan, Mauro Dragoni, and Chiara Ghidini. 2021. Process Extraction from Text: state of the art and challenges for the future. CoRR abs/2110.03754 (2021).

2. Anila Sahar Butt and Peter Fitch. 2020. ProvONE+: A Provenance Model for Scientific Workflows. In WISE(LNCS, Vol. 12343). Springer, 431–444.

3. Wellington Moreira de Oliveira, Daniel de Oliveira, and Vanessa Braganholo. 2018. Provenance Analytics for Workflow-Based Computational Experiments: A Survey. ACM Comput. Surv. 51, 3 (2018), 53:1–53:25.

4. The Publishing Workflow Ontology (PWO)

5. Abstract, link, publish, exploit: An end to end framework for workflow sharing

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