对齐引导的最大表重叠大小估计
Alignment-Guided Largest Table Overlap Size Estimation
AI总结：
研究如何快速估计表间最大重叠大小，提出基于明确行列结构、表间对齐信号及降低对语料库特定值分布敏感度的ALORE方法，性能优于现有技术。
摘要：
快速估计表之间最大重叠的大小有助于在大型表存储库中进行分块和逐表查询检索。第一个也是最先进的估计器Armadillo通过独立嵌入每个表并通过嵌入相似度近似重叠率来提高效率。然而，异构存储库中的准确估计仍然受到三个挑战的限制……
英文摘要：
Fast estimation of the size of the largest overlap between tables enables blocking and query-by-table retrieval in large table repositories. The first and the state-of-the-art estimator Armadillo improves efficiency by embedding each table independently and approximating overlap ratio via embedding similarity. However, accurate estimation in heterogeneous repositories remains limited by three challenges: (C1) overlap depends on row-column structure, i.e., each matched cell must preserve both its row and column membership under a joint alignment of the two tables, but existing encodings leave this structure to be inferred indirectly; (C2) independent encoding provides no explicit channel for inter-table alignment signals, biasing prediction toward global similarity; (C3) naive value encodings overfit to corpus-specific distributions, causing cross-domain degradation. Hence, we propose ALORE, a scalable and domain-robust overlap ratio estimator built on three principles: (P1) explicitly represent row-column structure; (P2) expose inter-table alignment signals during training without expensive alignment search; (P3) reduce sensitivity to corpus-specific value distributions. ALORE instantiates these principles with a Two-View Row-Column Hypergraph encoder, alignment-guided objectives with inexpensive interaction signals, and a domain-robust value mapping. Experiments on multiple datasets spanning diverse domains and scales, including a large real-world corpus beyond prior benchmarks, show that ALORE outperforms the state of the art. ALORE reduces MAE by up to 55% overall and 69% in zero-shot transfer, while achieving up to 89x speedup. We further validate its effectiveness for query-by-table retrieval.
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