A Hybrid Approach for Natural Language Querying Segmentation for Tourism Ontology


  • A. Salaiwarkul Department of Computer Science and Information Technology, Naresuan University, Thailand.
  • S. Khruakong Centre for Real-time Information Networks, University of Technology Sydney, Australia.


Name Entity, Natural Language Query, Tourism Ontology, Trigram,


We propose an approach to the interpretation of natural language queries, particularly relevant to the tourist industry in Thailand, by applying natural language queries against a tourism ontology containing tourism information specific to Thailand, and in the Thai language. Queries in Thai language are difficult to segment into words and meaningful phrases given that Thai has no word separation, such as in European languages which have a space between words, meaning specific Thai natural language processing is required. This paper demonstrates the identification and comparison of various methodologies currently available for segmenting natural language phrases, which allowed us to develop a hybrid approach based on aspects of natural language processing drawn from the methods analyzed. Our primary contribution is the hybrid approach, which was applied particularly to the queries and questions likely to be posed by Thai tourists in Thai language. Our discussion presents and describes the framework of the proposed methodology, which applies Thai Word Segmentation together with Trigram and the Name Entity method, together with our evaluation of the query response accuracy achieved, which collectively was 99%. We are confident that the proposed approach can be applied for developing any semantic searching application that allows natural language query, otherwise than for tourism in Thailand, our immediate concern.


D. Buhalis and S. H. Jun, "E-tourism," Contemporary tourism reviews, pp. 2-38, 2011.

L. Sebastia, I. Garcia, E. Onaindia, and C. Guzman, "e-Tourism: a tourist recommendation and planning application," International Journal on Artificial Intelligence Tools, vol. 18, pp. 717-738, 2009.

G. G. Chowdhury, "Natural language processing," Annual review of information science and technology, vol. 37, pp. 51-89, 2003.

S. Iwasaki and I. P. Horie, A reference grammar of Thai: Cambridge University Press, 2005.

T. Tran, P. Cimiano, S. Rudolph, and R. Studer, "Ontology-based interpretation of keywords for semantic search," in The Semantic Web, ed: Springer, 2007, pp. 523-536.

D. Bonino, F. Corno, L. Farinetti, and A. Bosca, "Ontology driven semantic search," WSEAS Transaction on Information Science and Application, vol. 1, pp. 1597-1605, 2004.

R. Guha, R. McCool, and E. Miller, "Semantic search," in Proceedings of the 12th international conference on World Wide Web, 2003, pp. 700-709.

S. Bechhofer, "OWL: Web ontology language," in Encyclopedia of Database Systems, ed: Springer, 2009, pp. 2008-2009.

W. V. Siricharoen, "Using Ontologies for E-tourism," in The 4th WSEAS/IASME International Conference on Engineering Education (EE 2007) Proceeding, 2007, pp. 113-118.

R. Jakkilinki, M. Georgievski, and N. Sharda, "Connecting destinations with an ontology-based e-tourism planner," Information and communication technologies in tourism 2007, pp. 21-32, 2007.

S. Khruahong, X. Kong, and D. Hoang, "Ontology Design for Thailand Travel Industry," International Journal of Knowledge Engineering, vol. vol. 1, no. 3, pp. 191-196, 2015.

Q. Guo and M. Zhang, "Question answering system based on ontology and semantic web," in International Conference on Rough Sets and Knowledge Technology, 2008, pp. 652-659.

K. Chaonithi and S. Prom-on, "A hybrid approach for Thai word segmentation with crowdsourcing feedback system," in Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2016 13th International Conference on, 2016, pp. 1-6.

S. Poria, E. Cambria, G. Winterstein, and G.-B. Huang, "Sentic patterns: Dependency-based rules for concept-level sentiment analysis," Knowledge-Based Systems, vol. 69, pp. 45-63, 2014.

C. Haruechaiyasak and S. Kongyoung, "TLex: Thai lexeme analyser based on the conditional random fields," in Proceedings of 8th International Symposium on Natural Language Processing, 2009.

C. Haruechaiyasak, S. Kongyoung, and M. Dailey, "A comparative study on thai word segmentation approaches," in Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on, 2008, pp. 125-128.

N. Wanichayapong, W. Pruthipunyaskul, W. Pattara-Atikom, and P. Chaovalit, "Social-based traffic information extraction and classification," in ITS Telecommunications (ITST), 2011 11th International Conference on, 2011, pp. 107-112.

C. Haruechaiyasak and A. Kongthon, "LexToPlus: A thai lexeme tokenization and normalization tool," WSSANLP-2013, p. 9, 2013.

E. Prud and A. Seaborne. (2006, 15 Jan 2017). SPARQL query language for RDF. Available: http://www.w3.org/TR/rdf-sparql-query

B. Quilitz and U. Leser, "Querying distributed RDF data sources with SPARQL," in European Semantic Web Conference, 2008, pp. 524-538.




How to Cite

Salaiwarkul, A., & Khruakong, S. (2018). A Hybrid Approach for Natural Language Querying Segmentation for Tourism Ontology. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-5), 109–113. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3640