• Tassanan Treenuntharath คณะวิทยาศาสตร์และเทคโนโลยี สาขาวิชาเทคโนโลยีสารสนเทศ มหาวิทยาลัยราชภัฏเพชรบูรณ์
  • Thinaphan Nithiyuwith


Smart Library, Ontology, Machine Learning


Real-time analysis, prediction, and recommendation for all library services to members are necessary for smart library development. This process is the important question in this research by having objectives 1) to study the requirement and data structure in smart library system 2) to develop the prediction process in the smart library by integrating ontology and machine learning and 3) to assess the performance of prediction in the smart library by integrating ontology and machine learning. We studied the system requirements from 30 librarians and members and collected data structure in library service transactions by purposive sampling from Academic Resource and Information Technology Center because these universities collected data in electronic database and defined smart library development plan. We developed the new prediction process using the ontology send semantic data to train and predict the classification algorithm. The result of performance assessment founded that K-NN (K=3) can accurately predict the book order at F-score 100%, and the system can provide the predictive oriented recommendation.


Academic Resource and Information Technology Center. Summary Report of Library Services in Year 2018 – 2019. Phetchabun: Phetchabun Rajabhat University, 2019.

Chang CC, Lin CJ. A Library for Support Vector Machines (LibSVM). ACM Transactions on Intelligent Systems and Technology (TIST). 2011; 2(3): 1-39.

Chongchorhor C. Using Benefits from Ontology in Information Services. Dissertation of Philosophy (Information Studies), Faculty of Humanities and Social Sciences, Khonkhan University, 2011.

Intarat K, Sillaparat S. Tropical Mangrove Species Classification Using Random Forest Algorithm and Very High-Resolution Satellite Imagery, Journal of Burapha Science. 2019; 24(2): 742-753.

Lai P, Phan N, Hu H. et al. Ontology-based Interpretable Machine Learning for Textual Data. Proceedings of International Joint Conference on Neural Networks (IJCNN); 2020 July 19-24; Glasgow, UK. 2020, 1-10.

Ministry of University Affairs. Higher Education Institution Library Standards. Bangkok: Ministry of University Affairs, 2015.

Musen M. The Protégé Project: A Look Back and a Look Forward, AI Matters. 2015; 1(4): 4-12.

Thanomsieng N. Simple Logistic Regression Analysis. Khon Kaen: Khon Kaen University, 2015.

Puengsom S. Semantic Web Patent Search Engine on Ontology Cade Study Medicine. Master of Computer Science and Information, Graduate School, Silpakorn University, 2013.

Reynolds D. Library Automation: Issues and Applications Hardcover. United state : R.R. Bowker, 1985.

Sillapa W, Punpocha S, Puangkerd B. Forecasting Stock Price Using Backpropagation Algorithm and Nonlinear Autoregressive Exogenous Model (NARX). Proceedings of the Research Presentation in Graduation Level 2017, 1508-1518.

Sinsomboontong S. Data mining 1st ed. Bangkok: Chamchuri Products, 2015.

Sivilai S, Snae J. The Development of a Question-Answer System for Recommending Appropriate Food for Patients. Proceedings of The 8th Naresuan Research: Innovative Knowledge for ASEAN Community. 2013 February 26-27; Phetchaburi, Thailand. 2013: 167-172.

Sun B, Du J, Gao T. Study on the Improvement of K-Nearest-Neighbor Algorithm. Proceedings of International Conference on Artificial Intelligence and Computational Intelligence; 7-8 Noverber 2009; Shanghai, China; 2009, 390-393.

Tresp V, Bundschus M, Rettinger A. et al. Towards Machine Learning on the Semantic Web. Proceedings of International Workshop on Uncertainty Reasoning for the Semantic Web. 2008 October 1; 2008: 282-314.

Wipawin N. Social network in a networked society, TLA Research Journal. 2015; 8(2): 119-127.

Yu K, Gong R, Sun L. et al. The Application of Artificial Intelligence in Smart Library. Proceedings of International Conference of Organizational Innovation (ICOI 2019). 2019 July 20-22; University of Ulsan, South Korea. 2019, 708-713.

Zhang H, Guo Y, Li Q. et al. An Ontology-Guided Semantic Data Integration Framework to Support Integrative Data Analysis of Cancer Survival. Proceedings of The 2nd International Workshop on Semantics-Powered Data Analytics Kansas City. 2017 November 13; MO, USA. 2018, 130-157.




How to Cite

Treenuntharath , T., & Nithiyuwith, T. (2020). THE DEVELOPMENT OF PREDICTION PROCESS IN SMART LIBRARY SYSTEM BY INTEGRATING ONTOLOGY AND MACHINE LEARNING . Life Sciences and Environment Journal, 21(2), 441–452. Retrieved from



Research Articles