Semantic reasoning for smog disaster analysis

Jiaoyan Chen, Huajun Chen, Jeff Z. Pan

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

Abstract

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
EditorsM Lenzerini, R Penaloza
PublisherCEUR-WS
Volume1577
ISBN (Print)1613-0073
Publication statusPublished - 2016
Event29th International Workshop on Description Logics, DL 2016 - Cape Town, South Africa
Duration: 22 Apr 201625 Apr 2016

Conference

Conference29th International Workshop on Description Logics, DL 2016
CountrySouth Africa
CityCape Town
Period22/04/1625/04/16

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Chen, J., Chen, H., & Pan, J. Z. (2016). Semantic reasoning for smog disaster analysis. In M. Lenzerini, & R. Penaloza (Eds.), DL 2016 International Workshop on Description Logics: Proceedings of the 29th International Workshop on Description Logics (Vol. 1577). CEUR-WS. http://ceur-ws.org/Vol-1577/