Volume 18 Number 1 (Feb. 2023)
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JSW 2022 Vol.18(1): 1-14
doi: 10.17706/jsw.18.1.1-14

Pipeline for the Automatic Extraction of Procedural Knowledge from Assembly Instructions into Controlled Natural Language

Christine Rese1,2*, Nikolai West2, Mathias Gebler1, Sven Krzoska1,2, Philipp Schlunder3, Jochen Deuse3,4

1 Volkswagen AG, Germany.
2 Institute for Production Systems, Germany.
3 RapidMiner GmbH, Germany.
4 University of Technology Sydney, Australia.

Abstract—This paper presents the application of a Natural Language Processing (NLP) pipeline, which automatically extracts procedural knowledge in a standardized way from assembly instructions. The developed pipeline is able to parse and process written German assembly instructions regardless of the language discourse. The pipeline helps resolve ambiguities in assembly instructions by converting them into a Controlled Natural Language (CNL). The pipeline fully automates the translation process from free-text assembly instructions to CNL representations. We investigated and evaluated the efficiency and robustness of the NLP pipeline along multiple dimensions, such as different assembly process designers, language and fuzzy string matching models. To test the developed pipeline we used to automatically extract procedural knowledge in a standardized way for 2,740 assembly instructions obtained from automotive industry. Our investigation shows that the NLP pipeline is able to extract CNL representations with high accuracy (ø 87%). Downstream applications, such as assembly line balancing, can reuse the uniformly extracted procedural knowledge.
Index Terms—Controlled Natural Language, Industrial Data Science, Information Extraction, Natural Language
Processing, Text Mining


Cite: Christine Rese*, Nikolai West, Mathias Gebler, Sven Krzoska, Philipp Schlunder, Jochen Deuse, "TPipeline for the Automatic Extraction of Procedural Knowledge from Assembly Instructions into Controlled Natural Language," Journal of Software vol. 18, no. 1, pp. 1-14, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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