Urban Digital Twins (UDT) depend on integrating various heterogeneous data sources to represent complex urban environments. Metadata management is an essential component of such systems. This paper introduces a framework for analyzing and visualizing semantic relationships between digital resources in the UDT metadata catalogs, focusing on the Smart District Data Infrastructure (SDDI). Conversion of metadata relationships into directed graphs enables intuitive exploration of the dependencies between resources. Utilizing technologies such as CKAN, NetworkX, Cytoscape, and Dash allows users to perform advanced analytic tasks such as detecting important resources, community detection, detection of isolated resources, cycle detection, and link prediction. It will show how graph theory can help in improving the quality evaluation of metadata, enhance transparency and usability, and contribute to the scalable and effective implementation of UDTs.