Unravelling the Potential of Knowledge Graphs and Graph Neural Networks: Techniques and Applications

In today’s digital economy, organisations must manage an excessive volume of unstructured data. Traditional data warehouses are frequently underutilised due to inconsistencies in how to successfully evaluate such massive amounts of information. Knowledge graphs have developed as an effective solution, giving a structured representation of interrelated facts to improve data organisation, retrieval, and inference across several disciplines. Knowledge graphs, which capture interactions between elements such as people, locations, and concepts, provide a complete framework for interpreting and identifying hidden patterns in data. This study investigates the evolution of knowledge graphs, from simple data integration tools to complex systems that support semantic searches. These searches go beyond standard keyword searches, providing context-aware results that increase the relevance and accuracy of information retrieval. In addition, the study investigates the use of knowledge graphs in recommendation systems, emphasising their capacity to personalise user experiences through personalised suggestions. This research highlights the potential of knowledge graph technology to improve data management and utilisation, promoting innovation and efficiency across a variety of sectors.