Predicting Chemical Accident Consequences from Substance Similarity: An On-Demand Ontology Approach

Ontology-based risk assessment usually infers what a query needs before the query is posed. For a material the knowledge graph does not contain, or when a query changes how similarity is measured, part of that information depends on the query itself. We propose an on-demand cycle that, when a query names a material, computes its hazard-profile similarity to known materials from GHS hazard statements, injects the result into the knowledge graph as validated individuals, and retrieves consequence types ranked by lift. Similarities among materials already in the graph can be precomputed, so the cycle serves chiefly materials outside it. On 12,274 accidents from five databases, the cycle ran end to end for chlorine, benzene, and germane, a material absent from the graph, in about 7 s per query. In leave-one-out validation over 1933 accident–material cases, a recorded consequence type was among the top three in 63.5% of cases, against 47.0% with random target materials, and in 72.9% of the cases missed by a most-frequent-type baseline, against 60.1%. The gain varied by consequence type and material group and awaits confirmation on independent data. Because it computes from GHS hazard classifications, on which chemical regulations also rely, the cycle points toward dynamic assessment that could support chemical safety management.