STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion
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.
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Shui-Hsiang Hsu
admin_assigned
2026-01-27T16:12:55.294Z
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Shui-Hsiang Hsu
adsl135789
user
adsl135789
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Tsung-Hsiang Chou
claimed_verified
2026-01-27T09:43:29.157Z
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Chou, TSUNG-HSIANG
yumeow122
user
yumeow122
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Chen-Jui Yu
claimed_verified
2026-01-27T13:57:03.798Z
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CHEN-JUI, YU
rui0828
user
rui0828
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Yao-Chung Fan
admin_assigned
2026-01-27T14:32:03.738Z
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Yao-Chung Fan
tomoto923
user
tomoto923
STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion
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.
STAR improves table representation through semantic clustering and weighted fusion to enhance query-table alignment in table retrieval tasks.
semantic clustering weighted fusion table retrieval semantic representation synthetic queries K-means clustering partial table embedding alignment