K-Hub: A Modular Ontology to Support Document Retrieval and Knowledge Extraction in Industry 5.0-Reference-Cited by-同舟云学术

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K-Hub: A Modular Ontology to Support Document Retrieval and Knowledge Extraction in Industry 5.0

Published:2023 Issue: Volume: Page:454-470
ISSN:0302-9743
Container-title:The Semantic Web
language:
Short-container-title:

Author:

Rula Anisa^ORCID,Re Calegari Gloria^ORCID,Azzini Antonia^ORCID,Bucci Davide^ORCID,Carenini Alessio^ORCID,Baroni Ilaria^ORCID,Celino Irene^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-33455-9_27

Reference24 articles.

1. Bach, N., Badaskar, S.: A survey on relation extraction. Lang. Technol. Inst. Carnegie Mellon University 178, 15 (2007)

2. Bellan, P., Dragoni, M., Ghidini, C.: Process extraction from text: state of the art and challenges for the future. CoRR abs/2110.03754 (2021)

3. Berrueta, D., Phipps, J.: Best Practice Recipes for Publishing RDF Vocabularies (2008). https://www.w3.org/TR/swbp-vocab-pub/

4. Lecture Notes in Computer Science;AS Butt,2020

5. Lecture Notes in Computer Science;O Corcho,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Procedural Text Mining with Large Language Models;Proceedings of the 12th Knowledge Capture Conference 2023;2023-12-05

2. Annotation and Extraction of Industrial Procedural Knowledge from Textual Documents;Proceedings of the 12th Knowledge Capture Conference 2023;2023-12-05

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