Alignment-Guided Largest Table Overlap Size Estimation
2026-07-03 • Computation and Language
Computation and LanguageDatabases
AI summaryⓘ
The authors created ALORE, a new method to quickly estimate how much two tables overlap, which helps with searching large table collections. They say existing methods struggle because they don’t fully consider how rows and columns match between tables, don’t directly compare tables during encoding, and get confused by data differences in various domains. ALORE addresses these by clearly representing table structure, using signals that guide alignment without heavy computation, and making the method less sensitive to domain differences. Their tests show ALORE is more accurate and much faster than previous methods, even working well when applied to new types of data.
table overlap estimationtable embeddingrow-column structureinter-table alignmentdomain robustnessquery-by-table retrievalzero-shot transferhypergraph encodermean absolute error (MAE)large-scale table repositories
Authors
Ge Lee, Shixun Huang, Zhifeng Bao, Shazia Sadiq, Yanchang Zhao
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.