Alignment-Guided Largest Table Overlap Size Estimation

Researchers propose ALORE, a scalable and domain-robust estimator for the size of the largest overlap between tables, addressing limitations of the state-of-the-art Armadillo. ALORE reduces mean absolute error by up to 55% overall and 69% in zero-shot transfer, while achieving up to 89x speedup, validated on diverse datasets including a large real-world corpus.

arXiv:2607.03049v1 Announce Type: new Abstract: 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.