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General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2018 Vol.4(2): 81-86 ISSN: 2382-6185
doi: 10.18178/ijke.2018.4.2.105

Know-Ont Based Ontology Modeling Approach for Skill Knowledge Extraction

Khananat Jaroenchai and Churee Techawut
Abstract—Ontology is an important tool for organizing information into categorized data for a semantic web search engine in order to create an ontology which can support both the collection and the presentation of skill knowledge. This study used a Know-Ont based ontology modeling approach (KOOM) on a case study of a basketball shooting technique to create semi-automatic ontology engineering, which is expected to help facilitate better understanding and interpretation of the multidimensional table in a knowledge engineering process and reduce the need for expert support. The efficiency evaluation of skill knowledge extraction framework based on KOOM shows the following values: Precision =0.72, Recall =0.71, Accuracy = 0.94 and F-Measure =0 17., which are considered to be efficient.

Index Terms—Ontology, ontology modeling, semantic web, knowledge engineering.

The authors are with the Department of Computer Science, Chiang Mai University, Chiang Mai, 50200 Thailand (e-mail: khananat09@gmail .com).


Cite: Khananat Jaroenchai and Churee Techawut, "Know-Ont Based Ontology Modeling Approach for Skill Knowledge Extraction," International Journal of Knowledge Engineering vol. 4, no. 2, pp. 81-86, 2018.

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