The Construction And Reasoning Of Ontology On Industrial Fire Decision-making

Along with all kinds of modern information technology in the industrial fire domain’swidespread application, the industrial fire domain interior has accumulated the massive firecontrol knowledge. However, these simple structure and lack of semantic knowledge hasseriously restricted the development of industrial fire command and decision. It becomes a veryimport and distress problem how to integrate the heterogeneous knowledge, how to get the fireinformation efficiently, and how to make correct fire command decision.This paper has built industrial fire heterogeneous knowledge into a standard formatKnowledge Base by using ontology technology, and has realized information sharing andtransparent access. We can search the fire knowledge quickly and accurately on the basis ofontology by logical reasoning technology. What’s more, the query results can be used to assistfirefighters in the fire fighting and rescue work, raise the level of modernization of the firedepartment fire decision-making, and improve the ability to quickly deal with the emergencies.Embarks from the above application background, the article has first developed thetraditional seven-step method depending on the guidelines of skeleton method and proposed anew method to construct industrial fire ontology that realized the fire prevention domaincomprehensive integration, by analyzing the relative knowledge of industrial fire and explosionaccidents. Then studied relative knowledge, extracted core concepts, made clear the hierarchy ofontology of industrial fire and completed the ontology construction in Protégé tool. Finally,constructed fire information inference rules on the bases of industrial fire ontology, and thenimplemented the function of semantic-based retrieval and search of the domain on the platformof Jena. The experimental results showed that semantic-based retrieval method not only solvedthe problem of semantic heterogeneity in the field of industrial fire, but also enhanced theprecision and recall rate of information retrieval of industrial fire domain.