Connecting Asset Stores with Graph Databases

As media organizations accumulate diverse data from various sources, effective data integration and retrieval becomes increasingly critical. Graph databases offer a flexible and efficient platform for integration by leveraging graph-based data models and querying mechanisms. Graph databases can seamlessly integrate disparate data sets regardless of their original format or structure. Knowledge can be added to the graph through the application of semantic meaning to the data. Machine learning (ML) algorithms can extract additional information from data to enhance knowledge graphs (KGs). This paper demonstrates how knowledge graphs offer unique capabilities for understanding patterns over collections of large data sets.

Print ISSN

1545-0279

Electronic ISSN

2160-2492

Published

2024-04

Content type

Original Research

Keywords

knowledge graphs, data integration, semantic understanding, machine learning

DOI

10.5594/JMI.2024/XBZC2345