STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion

TL;DR

STAR introduces a novel framework for semantic table representation that uses header-aware clustering and weighted fusion to improve table retrieval accuracy across multiple benchmarks.

Contribution

The paper presents STAR, a lightweight method that enhances semantic table representations through clustering and adaptive fusion, outperforming previous approaches like QGpT.

Findings

STAR achieves higher recall than QGpT on five benchmarks.

Semantic clustering improves the diversity of partial table samples.

Weighted fusion enhances fine-grained semantic alignment.

Abstract

Table retrieval is the task of retrieving the most relevant tables from large-scale corpora given natural language queries. However, structural and semantic discrepancies between unstructured text and structured tables make embedding alignment particularly challenging. Recent methods such as QGpT attempt to enrich table semantics by generating synthetic queries, yet they still rely on coarse partial-table sampling and simple fusion strategies, which limit semantic diversity and hinder effective query-table alignment. We propose STAR (Semantic Table Representation), a lightweight framework that improves semantic table representation through semantic clustering and weighted fusion. STAR first applies header-aware K-means clustering to group semantically similar rows and selects representative centroid instances to construct a diverse partial table. It then generates cluster-specific synthetic queries to comprehensively cover the table's semantic space. Finally, STAR employs weighted fusion strategies to integrate table and query embeddings, enabling fine-grained semantic alignment. This design enables STAR to capture complementary information from structured and textual sources, improving the expressiveness of table representations. Experiments on five benchmarks show that STAR achieves consistently higher Recall than QGpT on all datasets, demonstrating the effectiveness of semantic clustering and adaptive weighted fusion for robust table representation. Our code is available at https://github.com/adsl135789/STAR.