Semantic Reasoning for Smog Disaster Analysis
نویسندگان
چکیده
Smog disaster is a severe global problem. Although it has been investigated for decades in environmental sciences, the analysis of smog data recently becomes an open problem in fields like big data and artificial intelligence. In this paper, we present our study of utilizing semantic reasoning techniques for accurate and explanatory smog disaster prediction. To this end, we enriched the smog data streams with background knowledge by ontology modeling, inferred underlying knowledge like semantic assertions and rules, built consistent prediction models by embedding the knowledge (i.e., assertions and rules) in machine learning algorithms, and finally provided explanations by rule-based reasoning.
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