State of Art Survey for Deep Learning Effects on Semantic Web Performance

Abstract

One of the more significant recent major progress in computer science is the coevolution of deep learning and the Semantic Web. This subject includes research from various perspectives, including using organized information inside the neural network training method or enriching these networks with ontological reasoning mechanisms. By bridging deep learning and the Semantic Web, it is possible to enhance the efficiency of neural networks and open up exciting possibilities in science. This paper presents a comprehensive study of the closest previous researches, which combine the role of Deep Learning and the performance of the Semantic web, which ties together the Semantic Web and deep learning science with their applications. The paper also explains the adoption of an intelligent system in Semantic Deep Learning (SemDeep). As significant results obtained from previous works addressed in this paper, it can be notified that they focussed on real-time detection of phishing websites by HTML Phish. Also, the DnCNN, led by ResNet, achieved the best results, Res-Unit, UNet, and Deeper SRCNN, which recorded 88.5% SSIM, 32.01 percent PSNR 3.90 percent NRMSE.