Semantic web oriented personalized electronic commerce product suggestion system

Abstract

With the studies on semantic web technology, the interest in this technology has increased over time. Today, the importance of data has increased to be used for purposes such as optimization, increasing user experience and improving. This increase has brought with it the need for readability of data. At this point, the semantic web has increased the readability of information by putting large data sets in a format that machines can interpret like humans. In this study, an electronic commerce system has been developed that can suggest related products to users by making use of the literature review in which semantic web technology is used. In the system development phase, firstly accepted ontology creation methods were examined, and a method was selected to create the needed ontology. Within the scope of the study, first, the capabilities of the semantic web to meet the technological needs are introduced. Then the semantic web and the technologies used in it are explained. In addition, the tools needed to develop applications with the semantic web are explained. Application development steps are explained in the study. Information about the successfully developed system is given and thus the use of semantic web is exemplified. Finally, suggestions were shared to improve the designed system. Due to the rapid developments in electronic commerce in recent years, it can be stated that this study is important.

Keywords

• Electronic Commerce
• Semantic Web
• Ontology
• Web 3.0

References

1. [1] Purwanto, A. (2022). What is the role of customer behavior for electronic e-commerce and modern market visit intention? Journal of Information Systems and Management (JISMA), 1(6): 46-57.
2. [2] Peng, L., Zhang, W., Wang, X., Liang, S. (2019). Moderating effects of time pressure on the relationship between perceived value and purchase intention in social e-commerce sales promotion: considering the impact of product involvement. Information & Management, 56(2): 317-328.
3. [3] Qureshi, I., Fang, Y., Ramsey, E., McCole, P., Ibbotson, P., Compeau, D. (2009). Understanding online customer repurchasing intention and the mediating role of trust–an empirical investigation in two developed countries. European Journal of Information Systems, 18(3): 205-222.
4. [4] Qin, Z. (2009). Introduction to e-commerce. Tsinghua University Press, 4-8.
5. [5] Berners-Lee, T. Tim Berners-Lee’s Semantic Web, (accessed date 15 September 2022). https://www.researchgate.net/publication/307845029_Tim_Berners-Lee’s_Semantic_Web.
6. [6] Hazer, B. (2015). Semantic web based pharmaceuticals recommendation system. Erciyes University, Graduate School of Natural and Applied Sciences. Computer Engineering Department.
7. [7] Yu, L., Gerstner, R. A. (2014). Developer’s Guide to the Semantic Web, Publisher: Springer.
8. [8] Hebeler, J., Fisher, M., Blace, R., Perez-Lopez, A., Dean, M. (2009). Semantic Web Programming, Publisher: Wiley inc.

9. [9] Badr, Y., Chbeir, R., Abraham, A., Hassanien, A.E. (2010). Emergent Web Intelligence: Advanced Semantic Technologies, Publisher: Springer.
10. [10] Rodriguez-Muro, M., Kontchakov, R., Zakharyaschev, M. (2013). Ontology-Based Data Access: Ontop of Databases, Publisher: Springer.
11. [11] Baz, F. Ç., Meral, H. C. (2021). Web 3.0 – The use of semantic web studies in different fields: an application development example. Osmaniye Korkut Ata University Journal of the Institute of Science and Technology, 4(3): 408 – 421.
12. [12] Kalelkar, M., Churi, P., Kalelkar, D. (2014). Implementation of model-view-controller architecture pattern for business intelligence architecture. International Journal of Computer Applications, 102(12): 14.
13. [13] Berners-Lee, T., Hendler, J., Lassila, O. (2001). The semantic web. Scientific American, Inc.
14. [14] Uschold, M. (2003). Where are the semantics in the semantic web? AI Magazine, 24(3): 25 – 36.
15. [15] Gomez-Perez, A., Corcho, O. (2002). Ontology languages for the semantic web. IEEE Intelligent Systems, 54 – 60.
16. [16] Missikoff, M., Navigli, R., Velardi, P. (2002). Integrated approach to web ontology learning and engineering. IEEE Computer, 60 – 63.