A REVIEW OF KNOWLEDGE GRAPH AND GRAPH NEURAL NETWORK APPLICATION
Elda Xhumari, Suela Maxhelaku, Endri Xhina · 2022
Many learning activities include working with graph data, which offers a wealth of relational information between parts.Modeling physical systems, learning molecular fingerprints, predicting protein interfaces, and diagnosing illnesses all need the use of a model that can learn from graph inputs.In other fields, such as learning from non-structural data such as texts and images, reasoning on extracted structures (such as phrase dependency trees and image scene graphs) is a major topic that requires graph reasoning models.Graph neural networks (GNNs) are neural models that use message transmission between graph nodes to represent graph dependency.Variants of GNNs have recently showed ground-breaking performance on a variety of deep learning tasks.This paper represents a review of the literature on Knowledge Graphs and Graph Neural Networks, with a particular focus on Graph Embeddings and Graph Neural Networks applications as a powerful tool for organizing structured data and making sense of unstructured data, which can be applied to a variety of real-world problems.