Visual Domain Ontology using OWL Lite for Semantic Image Processing

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  • Additional Information
    • Publication Information:
      UIKTEN, 2019.
    • Publication Date:
      2019
    • Collection:
      LCC:Education
      LCC:Technology
    • Abstract:
      In this paper, a visual domain ontology (VDO) is constructed using OWL-Lite Language. The VDO passes through two execution phases, namely, construction and inferring phases. In the construction phase, OWL classes are initialized, with reference to annotated scenes, and connected by hierarchical, spatial, and content-based relationships (presence/absence of some objects depends on other objects). In the inferring phase, the VDO is used to infer knowledge about an unknown scene. This paper aims to use a standard language, namely, OWL, to represent non-standard visual knowledge; facilitate straightforward ontology enrichment; and define the rules for inferring based on the constructed ontology. The OWL standardizes the constructed knowledge and facilitates advanced inferring because it is built on top of the first-order logic and description logic. The VDO then allows an efficient representation and reasoning of complex visual knowledge. In addition to representation, the VDO enables easy extension, sharing, and reuse of the represented visual knowledge.
    • File Description:
      electronic resource
    • ISSN:
      2217-8309
      2217-8333
    • Relation:
      http://www.temjournal.com/content/82/TEMJournalMay2019_372_382.pdf; https://doaj.org/toc/2217-8309; https://doaj.org/toc/2217-8333
    • Accession Number:
      10.18421/TEM82-08
    • Rights:
      Journal Licence: CC BY-NC-ND
    • Accession Number:
      edsdoj.b6500ad400864b57acac225d2cd49dfb
  • Citations
    • ABNT:
      AHMAD ADEL ABU-SHAREHA; ALI ALSHAHRANI. Visual Domain Ontology using OWL Lite for Semantic Image Processing. TEM Journal, [s. l.], n. 2, p. 372, 2019. Disponível em: . Acesso em: 22 ago. 2019.
    • AMA:
      Ahmad Adel Abu-Shareha, Ali Alshahrani. Visual Domain Ontology using OWL Lite for Semantic Image Processing. TEM Journal. 2019;(2):372. doi:10.18421/TEM82-08.
    • APA:
      Ahmad Adel Abu-Shareha, & Ali Alshahrani. (2019). Visual Domain Ontology using OWL Lite for Semantic Image Processing. TEM Journal, (2), 372. https://doi.org/10.18421/TEM82-08
    • Chicago/Turabian: Author-Date:
      Ahmad Adel Abu-Shareha, and Ali Alshahrani. 2019. “Visual Domain Ontology Using OWL Lite for Semantic Image Processing.” TEM Journal, no. 2: 372. doi:10.18421/TEM82-08.
    • Harvard:
      Ahmad Adel Abu-Shareha and Ali Alshahrani (2019) ‘Visual Domain Ontology using OWL Lite for Semantic Image Processing’, TEM Journal, (2), p. 372. doi: 10.18421/TEM82-08.
    • Harvard: Australian:
      Ahmad Adel Abu-Shareha & Ali Alshahrani 2019, ‘Visual Domain Ontology using OWL Lite for Semantic Image Processing’, TEM Journal, no. 2, p. 372, viewed 22 August 2019, .
    • MLA:
      Ahmad Adel Abu-Shareha, and Ali Alshahrani. “Visual Domain Ontology Using OWL Lite for Semantic Image Processing.” TEM Journal, no. 2, 2019, p. 372. EBSCOhost, doi:10.18421/TEM82-08.
    • Chicago/Turabian: Humanities:
      Ahmad Adel Abu-Shareha, and Ali Alshahrani. “Visual Domain Ontology Using OWL Lite for Semantic Image Processing.” TEM Journal, no. 2 (2019): 372. doi:10.18421/TEM82-08.
    • Vancouver/ICMJE:
      Ahmad Adel Abu-Shareha, Ali Alshahrani. Visual Domain Ontology using OWL Lite for Semantic Image Processing. TEM Journal [Internet]. 2019 [cited 2019 Aug 22];(2):372. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.b6500ad400864b57acac225d2cd49dfb&custid=s8280428