FICE这个新模型，不用重训练就能用在没见过的知识图谱上，基数估计误差比之前最好的还低一半多，而且速度快到亚毫秒。
FICE是首个可泛化到完全未见知识图谱（包括未见关系）且无需重训练的BGP查询基数估计器。它基于因子图视角的图神经网络，用编码器生成实体和关系嵌入，再用解码器按查询连接拓扑预测对数基数。论文证明BGP基数是2-hop邻域的局部函数，支撑了局部消息传递编码器设计。在10个知识图谱上，FICE将整体中位q-error从最佳基线的13.54降至5.34，尾部表现全面领先。其推理延迟低于1毫秒，且通过邻域采样扩展到百万三元组规模。
Fully Inductive Cardinality Estimation
Query optimization of Basic Graph Patterns (BGP) SPARQL queries over Knowledge Graphs (KG) requires accurate cardinality estimation. Recently published learned estimators outperform statistics- and sampling-based approaches, but share a limitation preventing their adoption in real-world triplestores: they are transductive and require retraining when the underlying graph changes or when applied to new graphs. We present FICE (Fully Inductive Cardinality Estimation), the first learned cardinality estimator for BGP queries over KGs that generalizes to entirely unseen graphs (including unseen relations), without any retraining. FICE is a graph neural network (GNN) with two coupled components. First, an encoder GNN over a factor-graph view of the KG produces entity and relation embeddings. We prove that BGP cardinality is a local function of the 2-hop neighborhood around bound terms in this view, motivating the local message-passing encoder. A decoder GNN then composes these embeddings along the join topology of the query to predict log-cardinality. The encoder and decoder are trained jointly, making the embeddings specialized for cardinality estimation. FICE is trained using neighborhood sampling to scale to KGs with millions of triples, and decouples embedding generation from cardinality decoding to enable estimation latency below a millisecond. Compared to learned and non-learned baselines over 10 KGs, FICE reduces the overall median q-error from 13.54 (for the best competitor) to 5.34 and dominates all approaches in tail behavior.