Linked Open Data Framework for Ethnic Groups in Thailand Learning

Authors

DOI:

https://doi.org/10.3991/ijet.v15i10.13337

Keywords:

Linked Open Data, Framework, Ethnic Groups in Thailand, Learning

Abstract


The key significant worldview of the Semantic Web is Linked Open Data, another period of the World Wide Web that capacities to carry suggestions to information. An enormous number of both public and private foundations have dis-tributed their information following the Linked Open Data philosophies, or have done as such with information from different associations. To this degree, since the generation and production of Linked Open Data are thorough designing procedures that require high consideration so as to achieve high caliber, and since experience has uncovered that current general guidance is not constantly adequate to be applied to each area, this paper presents a lot of guidance system for creating and distributing Linked Open Data with regards to ethnic groups in Thailand to outside (TEG-LOD Framework). This framework offers an exhaustive depiction of the undertakings to perform, including a rundown of steps, tools that help in accomplishing the errand, different alternatives for achievement of the assignment, and best practices and proposals. Also, this paper exhibits a pilot model on the generation and distribution of Linked Open Data about ethnic groups in Thai-land, adhering to the available guidance, where the ethnic groups in Thailand are the property of the Princess Maha Chakri Sirindhorn Anthropology Center (SAC) have been made and distributed as Linked Open Data.

Author Biographies

Wirapong Chansanam, Khon Kaen University

Information Science Department

Kulthida Tuamsuk, Khon Kaen University

Information Science Department

Juthatip Chaikhambung, Khon Kaen University

Information Science Department

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Published

2020-06-01

How to Cite

Chansanam, W., Tuamsuk, K., & Chaikhambung, J. (2020). Linked Open Data Framework for Ethnic Groups in Thailand Learning. International Journal of Emerging Technologies in Learning (iJET), 15(10), pp. 140–156. https://doi.org/10.3991/ijet.v15i10.13337

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Section

Papers