Analyzing Knowledge Graph Innovations and Emerging AI technologies for Cultural Heritage Data Management

Knowledge graphs have become a vital tool for structuring and accessing cultural heritage data, offering new ways to connect and interpret historical information. As the volume and complexity of heritage data grow, integrating advanced computational methods becomes essential to enhance their accuracy, usability, and accessibility. This paper explores emerging trends in cultural heritage knowledge graphs, focusing on the role of artificial intelligence and machine learning in improving entity recognition, contextualization, and knowledge enrichment. It examines how natural language processing and deep learning techniques can refine data interpretation and automate updates, leading to dynamic, self-sustaining knowledge graphs. Additionally, the study highlights the integration of knowledge graphs with immersive technologies such as augmented reality and virtual reality, which offer interactive ways to engage with heritage content. Finally, it discusses the impact of linked open data initiatives in fostering crossinstitutional collaboration and global accessibility. These advancements collectively redefine how cultural heritage is studied and experienced, making it more interconnected, interactive, and adaptable to new discoveries.