Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 2 (2022)

DOI
10.11591/ijece.v12i2.pp1818-1830
ISSN
2722-2578, 2088-8708
Published
2022-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded.

Collaboration network of the authors

14 authors · 31 co-authorship links · 3 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universiti Malaysia Terengganu
  • National University of Malaysia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arifah Che AlhadiUniversiti Malaysia Terengganu3
Nur Fadila Akma MamatUniversiti Malaysia Terengganu2
Nazratul Naziah Mohd MuhaitUniversiti Malaysia Terengganu2
Zulaiha Ali OthmanNational University of Malaysia2
Aziz DeramanUniversiti Malaysia Terengganu1
Masita Abdul JalilUniversiti Malaysia Terengganu1
Wan Nural Jawahir Wan YussofUniversiti Malaysia Terengganu1
Noor Hafhizah Abd RahimUniversiti Malaysia Terengganu1
Rabiei MamatUniversiti Malaysia Terengganu1
Ghada Al-RawashdehUniversiti Malaysia Terengganu1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach</dc:title><dc:creator>Vidanage, Kaneeka</dc:creator><dc:creator>Mohamad Noor, Noor Maizura</dc:creator><dc:creator>Mohemad, Rosmayati</dc:creator><dc:creator>Abu Bakar, Zuriana</dc:creator><dc:description>&lt;jats:p&gt;Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i2.pp1818-1830</dc:identifier><dc:identifier>10.11591/ijece.v12i2.pp1818-1830</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 2 (2022); 1818</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/25422/15532</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>