Melola Discovery

← Melola Discovery

Jurnal SINTA 2

Using Graph Pattern Association Rules on Yago Knowledge Base

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 2 (2019) · pp. 162–175

DOI
10.5614/itbj.ict.res.appl.2019.13.2.6
ISSN
2338-5499, 2337-5787
Published
2019-09-30
Publisher
DRI - ITB
Language
eng

Abstract

The use of graph pattern association rules (GPARs) on the Yago knowledge base is proposed. Extending association rules for itemsets, GPARS can help to discover regularities between entities in a knowledge base. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a GPAR algorithm for creating the association rules. Our research resulted in 1114 association rules, with the value of standard confidence at 50.18% better than partial completeness assumption (PCA) confidence at 49.82%. Besides that the computation time for standard confidence was also better than for PCA confidence.

Keywords

Collaboration network of the authors

22 authors · 42 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.

  • Bandung Institute of Technology
  • Indonesia University of Education
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dwi Hendratmo WidyantoroBandung Institute of Technology4
Bambang Riyanto TrilaksonoBandung Institute of Technology3
E. Aminudin AzizIndonesia University of Education3
Armein Z. R. LangiBandung Institute of Technology2
Suhono Harso SupangkatBandung Institute of Technology2
Muhammad Haris MaulanaBandung Institute of Technology1
Yoanes BandungBandung Institute of Technology1
Herry Imanta SitepuBandung Institute of Technology1
Budi SulistyoBandung Institute of Technology1
Budi RahardjoBandung Institute of Technology1
Dimitri MahayanaBandung Institute of Technology1
Jan Wira Gotama PutraBandung Institute of Technology1
Ghoziyah Haitan RachmanBandung Institute of Technology1
Benhard SitohangBandung Institute of Technology1
Gusti Ayu Putri SaptawatiBandung Institute of Technology1
Nasy`an Taufiq Al GhifariBandung Institute of Technology1
Richard ReinhartBandung Institute of Technology1
Bariza HaqiBandung Institute of Technology1

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.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Using Graph Pattern Association Rules on Yago Knowledge Base</dc:title>
	<dc:creator>Wahyudi, Wahyudi</dc:creator>
	<dc:creator>Khodra, Masayu Leylia</dc:creator>
	<dc:creator>Prihatmanto, Ary Setijadi</dc:creator>
	<dc:creator>Machbub, Carmadi</dc:creator>
	<dc:subject xml:lang="en-US">association rule</dc:subject>
	<dc:subject xml:lang="en-US">graph pattern</dc:subject>
	<dc:subject xml:lang="en-US">knowledge base</dc:subject>
	<dc:subject xml:lang="en-US">PCA confidence</dc:subject>
	<dc:subject xml:lang="en-US">standard confidence</dc:subject>
	<dc:description xml:lang="en-US">The use of graph pattern association rules (GPARs) on the Yago knowledge base is proposed. Extending association rules for itemsets, GPARS can help to discover regularities between entities in a knowledge base. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a GPAR algorithm for creating the association rules. Our research resulted in 1114 association rules, with the value of standard confidence at 50.18% better than partial completeness assumption (PCA) confidence at 49.82%. Besides that the computation time for standard confidence was also better than for PCA confidence. </dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/7388</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.2.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 2 (2019); 162-175</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2019.13.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/7388/4337</dc:relation>
</oai_dc:dc>