Development of Keyword Suggestion for Thai Document Search
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Published:
Dec 27, 2021
Keywords:
Search Engine Ontology Dictionary Keyword Suggestion
Main Article Content
Abstract
The problem arises when users use a search engine for specific domain document. Users usually do not know the specific word(s) for searching but only know some words in the context. As the consequence, the search engine returns the unexpected documents. Hence, we develop the search engine system which suggests related keyword(s) for searching based on dictionary and ontology. In the experimental comparison, the results show that the suggested keyword users based on the ontology have the highest number of correct answers and have the lowest number of searching than unsuggested keyword users and suggested keyword users based on the dictionary.
Article Details
How to Cite
Songmuang, P., Kesamoon, C., Kedtiwerasak, R., Wandee, W., Kongkachandra, R., Gulyanon, S., Limprasert, W., & Luantangsrisuk, V. (2021). Development of Keyword Suggestion for Thai Document Search. Journal of Technology Management Rajabhat Maha Sarakham University, 8(2), 63–76. retrieved from https://ph02.tci-thaijo.org/index.php/itm-journal/article/view/244024
Section
บทความวิจัย
References
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[4] Gan, L. and Tu, W., 2014, Improving Query Expansion Using Wikipedia, pp. 143-146, 2014 International Conference on Management of e-Commerce and e-Government, Shanghai.
[5] Gillies, J. and Cailliau, R., 2000, How the Web Was Born: The Story of the World Wide Web. Oxford University Press, United Kingdom, 392 p.
[6] Kim, Y., & Croft, W. B., 2015, Improving Patent Search by Search Result Diversification. Paper presented at the Proceedings of the 2015 International Conference on The Theory of Information Retrieval, Northampton, Massachusetts, USA.
[7] Langville, A. N. and Meyer, C. D., 2006, Google's PageRank and Beyond: The Science of Search Engine Rankings, Princeton University Press, United Kingdom, 240 p.
[8] Lyman, P. and Varian, H. R., 2003, How Much Information 2003?, Available Source: http://www.sims.berkeley.edu/research/projects/how-much-info-2003/, June 14, 2020.
[9] N. Lin, V. A. Kudinov, H. M. Zaw and S. Naing, 2020, Query Expansion for Myanmar Information Retrieval Used by WordNet, 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), St. Petersburg and Moscow, Russia , pp. 395-399
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[12] Salton, G. and Buckley, C., 1988, Term-weighting Approaches in Automatic Text Retrieval, Inf. Process. Manage. 24(5): 513-523.
[13] Saravanan, M., Ravindran, B. & Raman, S., 2009, Improving legal information retrieval using an ontological framework, Artif Intell Law 17, 101–124.
[14] Teeramunkong, T., Sornlertlamvanich, V., Tanhermhong T. and Chinnan, W., 2000, Character Cluster Based Thai Information Retrieval, pp. 75-80, Proceedings of the fifth international workshop on Information retrieval with Asian languages (IRAL ’00), Assoc. Comp. Mach., New York.
[15] Toman, S., Abed, M., & Toman, Z.H., 2020, Cluster-Based Information Retrieval by using (K-means)- Hierarchical Parallel Genetic Algorithms Approach, TELKOMNIKA Telecommunication, Computing, Electronics and Control Vol. 19, No. 1, February 2021, pp. 349-356.
