Mining Sematic Association Rules from RDF Data

Nima Khodadadi^*1, M. G. El-Mahgoub², Rokaia M. Zaki³

¹ Department of Civil and Architectural Engineering, University of Miami,

Coral Gables, FL, USA

² Basic science department, Delta higher institute for engineering and technology,

 Mansoura, 35111, Egypt

³ Higher Institute of Engineering and Technology, Kafrelsheikh, Egypt;

³Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Egypt

Emails: nima.khodadadi@miami.edu; melmahgoub@hotmail.com; rukaia.emam@feng.bu.edu.eg

Abstract

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.

Keywords: RDF data; semantic association rules; mining; Apriori algorithm; confidence; support lift; visualizations; Semantic Web.