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Hazard and operability analysis (HAZOP) is a remarkable representative inindustrial safety engineering. However, a great storehouse of industrial safetyknowledge (ISK) in HAZOP reports has not been thoroughly exploited. In order toreuse and unlock the value of ISK and optimize HAZOP, we have developed a novelknowledge graph for industrial safety (ISKG) with HAZOP as the carrier throughbridging data science and engineering design. Specifically, firstly,considering that the knowledge contained in HAZOP reports of differentprocesses in industry is not the same, we creatively develope a general ISKstandardization framework, it provides a practical scheme for integrating HAZOPreports from various processes and uniformly representing the ISK with diverseexpressions. Secondly, we conceive a novel and reliable information extractionmodel based on deep learning combined with data science, it can effectivelymine ISK from HAZOP reports, which alleviates the obstacle of ISK extractioncaused by the particularity of HAZOP text. Finally, we build ISK triples andstore them in the Neo4j graph database. We take indirect coal liquefactionprocess as a case study to develop ISKG, and its oriented applications canoptimize HAZOP and mine the potential of ISK, which is of great significance toimprove the security of the system and enhance prevention awareness for people.ISKG containing the ISK standardization framework and the informationextraction model sets an example of the interaction between data science andengineering design, which can enlighten other researchers and extend theperspectives of industrial safety.

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