A semi-automatic approach to construct Vietnamese ontology from online text


  • Bao-An Nguyen Tra Vinh University
  • Don-Lin Yang Feng Chia University, Taiwan




ontology, concept discovery, conceptual relation, text mining, lexical pattern, natural language processing


An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with much needed resources. In such systems, ontology construction is one of the most important phases. Since there are abundant documents on the Internet, useful learning materials can be acquired openly with the use of an ontology.  However, due to the lack of system support for ontology construction, it is difficult to construct self-instructional materials for Vietnamese people. In general, the cost of manual acquisition of ontologies from domain documents and expert knowledge is too high. Therefore, we present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. In this system, we use the combination of statistics-based, data mining, and Vietnamese natural language processing methods to develop concept and conceptual relation extraction algorithms to discover knowledge from Vietnamese text documents. From the experiments, we show that our approach provides a feasible solution to build Vietnamese ontologies used for supporting systems in education.

Author Biographies

Bao-An Nguyen, Tra Vinh University

Department of Information Technology

Don-Lin Yang, Feng Chia University, Taiwan

Professor in the Department of Information Engineering and Computer Science



How to Cite

Nguyen, B.-A., & Yang, D.-L. (2012). A semi-automatic approach to construct Vietnamese ontology from online text. The International Review of Research in Open and Distributed Learning, 13(5), 148–172. https://doi.org/10.19173/irrodl.v13i5.1250