mlsanityData to 14 Oct 2026

A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process

arXiv 2111.13854 ↗First posted 27 Nov 2021cs.CLcited by 5

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Abstract (arXiv)

Hazard and operability analysis (HAZOP) is a remarkable representative in industrial safety engineering. However, a great storehouse of industrial safety knowledge (ISK) in HAZOP reports has not been thoroughly exploited. In order to reuse and unlock the value of ISK and optimize HAZOP, we have developed a novel knowledge graph for industrial safety (ISKG) with HAZOP as the carrier through bridging data science and engineering design. Specifically, firstly, considering that the knowledge contained in HAZOP reports of different processes in industry is not the same, we creatively develope a general ISK standardization framework, it provides a practical scheme for integrating HAZOP reports from various processes and uniformly representing the ISK with diverse expressions. Secondly, we conceive a novel and reliable information extraction model based on deep learning combined with data science, it can effectively mine ISK from HAZOP reports, which alleviates the obstacle of ISK extraction caused by the particularity of HAZOP text. Finally, we build ISK triples and store them in the Neo4j graph database. We take indirect coal liquefaction process as a case study to develop ISKG, and its oriented applications can optimize HAZOP and mine the potential of ISK, which is of great significance to improve the security of the system and enhance prevention awareness for people. ISKG containing the ISK standardization framework and the information extraction model sets an example of the interaction between data science and engineering design, which can enlighten other researchers and extend the perspectives of industrial safety.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 93 references that are in the map. First posted on arXiv 27 Nov 2021.

Builds on

11 of 93 references.

References · 11

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 1.

5 papers.

  1. 2024LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methodscited by 8
  2. 2022A Zipf's Law-based Text Generation Approach for Addressing Imbalance in Entity Extractioncited by 2
  3. 2022A new hazard event classification model via deep learning and multifractalcited by 2
  4. 2023Will sentiment analysis need subculture? A new data augmentation approachcited by 1
  5. 2022Yes, DLGM! A novel hierarchical model for hazard classificationcited by 0