A Data-Driven Approach to Art Price Prediction: Integrating Knowledge Graphs and Predictive Analytics

The art market is a complex and dynamic system, where accurately predicting art prices remains a significant challenge. This research proposes a novel, data-driven approach to art price prediction by integrating knowledge graphs with deep learning techniques. Knowledge graphs provide a structured representation of the relationships between key entities, such as artists, artworks, galleries, auction houses, and historical pricing data, allowing for a deeper understanding of the factors influencing art value. Deep learning models are employed to analyze the intricate patterns within this rich, interconnected data, aiming to improve the accuracy and robustness of price predictions. By combining these two powerful methodologies, this study seeks to offer a more comprehensive framework for understanding and forecasting trends in the art market, with the potential to enhance decision-making for investors, collectors, and professionals.