Semantic Reasoning for Smog Disaster Analysis

Jiaoyan Chen, Huajun Chen, Jeff Z. Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationDL 2016 International Workshop on Description Logics
Subtitle of host publicationProceedings of the 29th International Workshop on Description Logics
EditorsMaurizio Lenzerini, Rafael Peñaloza
Number of pages4
Publication statusPublished - 25 Apr 2016
Event29th International Workshop on Description Logics - Cape Town, South Africa
Duration: 22 Apr 201625 Apr 2016

Publication series

NameCEUR Workshop Proceedings
ISSN (Electronic)1613-0073


Conference29th International Workshop on Description Logics
Abbreviated titleDL 2016
Country/TerritorySouth Africa
CityCape Town
Internet address


  • Ontology
  • OWL
  • Rule
  • Semantic reasoning
  • Smog disaster


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