  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.
