Evidence-Guided Schema Normalization for Temporal Tabular Reasoning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918224097968128 |
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| author | Thanga, Ashish Dixit, Vibhu Shankarampeta, Abhilash Gupta, Vivek |
| author_facet | Thanga, Ashish Dixit, Vibhu Shankarampeta, Abhilash Gupta, Vivek |
| contents | Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems. We propose a SQL-based approach that involves (1) generating a 3NF schema from Wikipedia infoboxes, (2) generating SQL queries, and (3) query execution. Our central finding challenges model scaling assumptions: the quality of schema design has a greater impact on QA precision than model capacity. We establish three evidence-based principles: normalization that preserves context, semantic naming that reduces ambiguity, and consistent temporal anchoring. Our best configuration (Gemini 2.5 Flash schema + Gemini-2.0-Flash queries) achieves 80.39 EM, a 16.8\% improvement over the baseline (68.89 EM). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evidence-Guided Schema Normalization for Temporal Tabular Reasoning Thanga, Ashish Dixit, Vibhu Shankarampeta, Abhilash Gupta, Vivek Computation and Language Artificial Intelligence Information Retrieval Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems. We propose a SQL-based approach that involves (1) generating a 3NF schema from Wikipedia infoboxes, (2) generating SQL queries, and (3) query execution. Our central finding challenges model scaling assumptions: the quality of schema design has a greater impact on QA precision than model capacity. We establish three evidence-based principles: normalization that preserves context, semantic naming that reduces ambiguity, and consistent temporal anchoring. Our best configuration (Gemini 2.5 Flash schema + Gemini-2.0-Flash queries) achieves 80.39 EM, a 16.8\% improvement over the baseline (68.89 EM). |
| title | Evidence-Guided Schema Normalization for Temporal Tabular Reasoning |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2512.00329 |