We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. A framework to assist decision making in the problem formulation process is developed. We build a DM 3 ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential.
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