Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry
Causal domain knowledge is commonly documented using natural language either in unstructured or semi-structured forms. This study aims to increase the usability of causal domain knowledge in industrial documents by transforming the information into a more structured format. The paper presents our wo...
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MDPI AG
2025-02-01
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| author | Houssam Razouk Leonie Benischke Daniel Gärber Roman Kern |
| author_facet | Houssam Razouk Leonie Benischke Daniel Gärber Roman Kern |
| author_sort | Houssam Razouk |
| collection | DOAJ |
| description | Causal domain knowledge is commonly documented using natural language either in unstructured or semi-structured forms. This study aims to increase the usability of causal domain knowledge in industrial documents by transforming the information into a more structured format. The paper presents our work on developing automated methods for causal information extraction from real-world industrial documents in the semiconductor manufacturing industry, including presentation slides and FMEA (Failure Mode and Effects Analysis) documents. Specifically, we evaluate two types of causal information extraction methods: single-stage sequence tagging (SST) and multi-stage sequence tagging (MST). The presented case study showcases that the proposed MST methods for extracting causal information from industrial documents are suitable for practical applications, especially for semi-structured documents such as FMEAs, with a 93% F1 score. Additionally, the study shows that extracting causal information from presentation slides is more challenging. The study highlights the importance of choosing a language model that is more aligned with the domain and in-domain pre-training. |
| format | Article |
| id | doaj-art-ff2e03c422cc4e4abc0f3af71bb1d0cd |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ff2e03c422cc4e4abc0f3af71bb1d0cd2025-08-20T02:05:08ZengMDPI AGApplied Sciences2076-34172025-02-01155257310.3390/app15052573Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing IndustryHoussam Razouk0Leonie Benischke1Daniel Gärber2Roman Kern3Infineon Technologies Austria AG, 9500 Villach, AustriaInfineon Technologies Austria AG, 9500 Villach, AustriaInstitute of Machine Learning and Neural Computation, Graz University of Technology, 8010 Graz, AustriaInstitute of Machine Learning and Neural Computation, Graz University of Technology, 8010 Graz, AustriaCausal domain knowledge is commonly documented using natural language either in unstructured or semi-structured forms. This study aims to increase the usability of causal domain knowledge in industrial documents by transforming the information into a more structured format. The paper presents our work on developing automated methods for causal information extraction from real-world industrial documents in the semiconductor manufacturing industry, including presentation slides and FMEA (Failure Mode and Effects Analysis) documents. Specifically, we evaluate two types of causal information extraction methods: single-stage sequence tagging (SST) and multi-stage sequence tagging (MST). The presented case study showcases that the proposed MST methods for extracting causal information from industrial documents are suitable for practical applications, especially for semi-structured documents such as FMEAs, with a 93% F1 score. Additionally, the study shows that extracting causal information from presentation slides is more challenging. The study highlights the importance of choosing a language model that is more aligned with the domain and in-domain pre-training.https://www.mdpi.com/2076-3417/15/5/2573causal information extractioncausal relation extractionnatural language processingpresentation slidesFMEAsemiconductor manufacturing |
| spellingShingle | Houssam Razouk Leonie Benischke Daniel Gärber Roman Kern Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry Applied Sciences causal information extraction causal relation extraction natural language processing presentation slides FMEA semiconductor manufacturing |
| title | Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry |
| title_full | Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry |
| title_fullStr | Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry |
| title_full_unstemmed | Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry |
| title_short | Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry |
| title_sort | increasing the accessibility of causal domain knowledge via causal information extraction methods a case study in the semiconductor manufacturing industry |
| topic | causal information extraction causal relation extraction natural language processing presentation slides FMEA semiconductor manufacturing |
| url | https://www.mdpi.com/2076-3417/15/5/2573 |
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