A Novel Approach to Populate Multimedia Knowledge Graph via Deep Learning and Semantic Analysis
Antonio M. Rinaldi, Cristiano Russo, Cristian Tommasino · 2022
The growth of data in volume and complexity needs automatic tools to manage and process information. Semantic Web Technologies are a silver bullet in this context due to their capacity to transform human-readable contents into machine-readable ones. Knowledge graphs and the related ontologies represent essential tools for managing very large knowledge bases. The population process of these knowledge structures is composed of expensive and time-consuming tasks, and we propose a novel approach to automate the population step. Our approach is based on novel techniques based on semantic analysis and deep learning using NoSQL technologies. Several results to show the effectiveness of our approach is also reported.