1
Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA
(nima.khodadadi@miami.edu)
2
Basic science department, Delta higher institute for engineering and technology, Mansoura, 35111, Egypt
(melmahgoub@hotmail.com)
3
Higher Institute of Engineering and Technology, Kafrelsheikh, Egypt; Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Egypt
(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.
References :
[1] Asadifar, Somayyeh, and Mohsen Kahani, Semantic association rule mining: a new approach for stock market prediction. 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), IEEE, 2017.
[2] Barati Molood, Quan Bai, and Qing Liu, Mining semantic association rules from RDF data. Knowledge-Based Systems, 133, 183-196, 2017.
[3] Barati, Molood, Quan Bai, and Qing Liu, SWARM: An approach for mining semantic association rules from semantic web data. PRICAI 2016: Trends in Artificial Intelligence: 14th Pacific Rim International Conference on Artificial Intelligence, Phuket, Thailand, August 22-26, 2016.
[4] Heba R. Abdelhady, Mahmoud M. Ismail, Cardiovascular Diseases Forecasting using Machine Learning Models. Journal of International Journal of Advances in Applied Computational Intelligence, 1(2) , 56-62, 2022.
[5] Alfrjani, Rowida, Taha Osman, and Georgina Cosma, A new approach to ontology-based semantic modelling for opinion mining. 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation (UKSim), IEEE, 2016.
[6] LePendu, Paea, et al., Ontology database: A new method for semantic modeling and an application to brainwave data. Scientific and Statistical Database Management: 20th International Conference, SSDBM 2008, Hong Kong, China, July 9-11, 2008 Proceedings 20. Springer Berlin Heidelberg, 2008.
[7] Mohamed Saber, Efficient phase recovery system, IJEECS, 5(1), 2017.
[8] Alber S. Aziz, ,Hoda K. Mohamed, Ahmed Abdelhafeez, Unveiling the Power of Convolutional Networks: Applied Computational Intelligence for Arrhythmia Detection from ECG Signals. Journal of International Journal of Advances in Applied Computational Intelligence, 1(2), 63-72, 2022.
[9] Douali, Nassim, et al., Case based fuzzy cognitive maps (CBFCM): new method for medical reasoning: comparison study between CBFCM/FCM. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011). IEEE, 2011.
[10] Ismail Eyad Samara, Intelligent systems and AI techniques: Recent advances and Future directions. Journal of International Journal of Advances in Applied Computational Intelligence, 1(2), 30-45, 2022.
[11] Mohamed Saber, A novel design and implementation of FBMC transceiver for low power applications. IJEEI, 8(1), 83-93, 2020.
[12] Sirichanya, Chanmee, and Kesorn Kraisak. Semantic data mining in the information age: A systematic review. International Journal of Intelligent Systems 36(8), 3880-3916, 2021.
[13] Abdulla Alsharhan, Natural Language Generation and Creative Writing A Systematic Review. Journal of International Journal of Advances in Applied Computational Intelligence, 1(1), 69-90, 2022.
[14] Hani D. Hejazi, Ahmed A. Khamee, Employees Motivational Factors toward Knowledge Sharing: A Systematic Review. Journal of International Journal of Advances in Applied Computational Intelligence, 1(1), 45-68, 2022.
| Style | # |
|---|---|
| MLA | Nima Khodadadi, M. G. El-Mahgoub, Rokaia M. Zaki. "Mining Sematic Association Rules from RDF Data." Journal of Artificial Intelligence and Metaheuristics, Vol. 4, No. 1, 2023 ,PP. 43-51 (Doi : https://doi.org/10.54216/JAIM.040105) |
| APA | Nima Khodadadi, M. G. El-Mahgoub, Rokaia M. Zaki. (2023). Mining Sematic Association Rules from RDF Data. Journal of Journal of Artificial Intelligence and Metaheuristics, 4 ( 1 ), 43-51 (Doi : https://doi.org/10.54216/JAIM.040105) |
| Chicago | Nima Khodadadi, M. G. El-Mahgoub, Rokaia M. Zaki. "Mining Sematic Association Rules from RDF Data." Journal of Journal of Artificial Intelligence and Metaheuristics, 4 no. 1 (2023): 43-51 (Doi : https://doi.org/10.54216/JAIM.040105) |
| Harvard | Nima Khodadadi, M. G. El-Mahgoub, Rokaia M. Zaki. (2023). Mining Sematic Association Rules from RDF Data. Journal of Journal of Artificial Intelligence and Metaheuristics, 4 ( 1 ), 43-51 (Doi : https://doi.org/10.54216/JAIM.040105) |
| Vancouver | Nima Khodadadi, M. G. El-Mahgoub, Rokaia M. Zaki. Mining Sematic Association Rules from RDF Data. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 4 ( 1 ): 43-51 (Doi : https://doi.org/10.54216/JAIM.040105) |
| IEEE | Nima Khodadadi, M. G. El-Mahgoub, Rokaia M. Zaki, Mining Sematic Association Rules from RDF Data, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 4 , No. 1 , (2023) : 43-51 (Doi : https://doi.org/10.54216/JAIM.040105) |