Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry

Houssam Razouk*, Leonie Benischke, Daniel Gärber, Roman Kern

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number2573
JournalApplied Sciences
Volume15
Issue number5
Early online date27 Feb 2025
DOIs
Publication statusPublished - Mar 2025

Keywords

  • causal information extraction
  • causal relation extraction
  • FMEA
  • natural language processing
  • presentation slides
  • semiconductor manufacturing

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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