<ul data-eligibleForWebStory="false"><li>Neural graph databases (NGDBs) are efficient data retrieval mechanisms for deep learning-based models to access precise information.</li><li>Current NGDBs are limited to single-graph operation, hindering reasoning across multiple distributed graphs.</li><li>The lack of support for multi-source graph data in existing NGDBs affects reasoning across distributed sources, impacting decision-making.</li><li>Proposed solution, Federated Neural Graph DataBase (FedNGDB), uses federated learning for privacy-preserving reasoning over multi-source graph data, improving data quality.</li></ul>

Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs

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