{"slug": "alignment-guided-largest-table-overlap-size-estimation", "title": "Alignment-Guided Largest Table Overlap Size Estimation", "summary": "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.", "body_md": "arXiv:2607.03049v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/alignment-guided-largest-table-overlap-size-estimation", "canonical_source": "https://arxiv.org/abs/2607.03049", "published_at": "2026-07-07 04:00:00+00:00", "updated_at": "2026-07-07 04:02:10.043995+00:00", "lang": "en", "topics": ["machine-learning", "ai-research"], "entities": ["ALORE", "Armadillo"], "alternates": {"html": "https://wpnews.pro/news/alignment-guided-largest-table-overlap-size-estimation", "markdown": "https://wpnews.pro/news/alignment-guided-largest-table-overlap-size-estimation.md", "text": "https://wpnews.pro/news/alignment-guided-largest-table-overlap-size-estimation.txt", "jsonld": "https://wpnews.pro/news/alignment-guided-largest-table-overlap-size-estimation.jsonld"}}