Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1943 2022 2022 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA Nima Khodadadi Basic science department, Delta higher institute for engineering and technology, Mansoura, 35111, Egypt M. G. El El-Mahgoub Higher Institute of Engineering and Technology, Kafrelsheikh, Egypt; Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Egypt Rokaia M. Zaki Many fields rely heavily on the accurate and consistent portrayal of structured data. In order to effectively express and link information on the Semantic Web, RDF (Resource Description Framework) data is essential. Here, we present a process for extracting semantic association rules from RDF data. For our method, we employ the Apriori algorithm to mine the RDF triples for hidden connections between ideas and relationships. Using metrics such as confidence, support, and lift, we examine how well our model performs. We also give visual representations, like as scatter plots and clustered matrices, to make the correlations easier to understand and analyse. The findings validate our model's potential to unearth significant relationships, which in turn reveal important details about the RDF data's underlying semantics. Our findings are discussed, and suggestions for further study are provided. 2023 2023 43 51 10.54216/JAIM.040105 https://www.americaspg.com/articleinfo/28/show/1943