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Leveraging graph-based semantic annotation for the identification of cause-effect relations

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 4 (2020)

DOI
10.11591/ijece.v10i4.pp4352-4362
ISSN
2722-2578, 2088-8708
Published
2020-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This research is related to language article in Indonesia that discuss about causality relationship research used as public health surveillance information monitoring system. Utilization of this research is suitability of feature selection, phrase annotation, paragraph annotation, medical element annotation and graph-based semantic annotation. Evaluation of system performance is done by intrinsic approach using the Naive Bayes Multinomial method. The results obtained sequentially for recall, precision and f-measure are 0.924, 0.905, and 0.910.

Collaboration network of the authors

4 authors · 4 co-authorship links

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  • Bandung Institute of Technology
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Suhono Harso SupangkatBandung Institute of Technology1
Saiful AkbarBandung 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.

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>Leveraging graph-based semantic annotation for the identification of cause-effect relations</dc:title><dc:creator>Bhaskoro, Susetyo Bagas</dc:creator><dc:creator>Hardini, Inkreswari Retno</dc:creator><dc:description>&lt;jats:p&gt;This research is related to language article in Indonesia that discuss about causality relationship research used as public health surveillance information monitoring system. Utilization of this research is suitability of feature selection, phrase annotation, paragraph annotation, medical element annotation and graph-based semantic annotation. Evaluation of system performance is done by intrinsic approach using the Naive Bayes Multinomial method. The results obtained sequentially for recall, precision and f-measure are 0.924, 0.905, and 0.910.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-08-01</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.v10i4.pp4352-4362</dc:identifier><dc:identifier>10.11591/ijece.v10i4.pp4352-4362</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 4 (2020); 4352</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/21491/14128</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>