NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training

Neural Graph Databases (NGDBs) support complex logical reasoning over incomplete knowledge structures, yet their training efficiency and expressivity are constrained by rigid query-level batching and structure-only embeddings. We present NGDB-Zoo, a unified framework that resolves these bottlenecks by synergizing operator-level training with semantic augmentation. By decoupling logical operators from query topologies, NGDB-Zoo transforms training loops into dynamically scheduled data-flow executions, enabling multi-stream parallelism and achieving a $1.8\times$-$6.8\times$ average throughput compared to baselines. Furthermore, we formalize a decoupled architecture to integrate semantic priors from pre-trained text embeddings without triggering I/O stalls or memory overflows. Experiments on six benchmarks, including $\text{ogbl-wikikg2}$ and $\text{ATLAS-Wiki}$, show that NGDB-Zoo scales dense query-embedding training to million-entity graphs while maintaining competitive filtered MRR.