SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917196201984000 |
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| author | Mulang, Isaiah Onando Sasaki, Felix Klein, Tassilo Kolk, Jonas Grechanov, Nikolay Hoffart, Johannes |
| author_facet | Mulang, Isaiah Onando Sasaki, Felix Klein, Tassilo Kolk, Jonas Grechanov, Nikolay Hoffart, Johannes |
| contents | Building upon the SALT benchmark for relational prediction (Klein et al., 2024), we introduce SALT-KG, a benchmark for semantics-aware learning on enterprise tables. SALT-KG extends SALT by linking its multi-table transactional data with a structured Operational Business Knowledge represented in a Metadata Knowledge Graph (OBKG) that captures field-level descriptions, relational dependencies, and business object types. This extension enables evaluation of models that jointly reason over tabular evidence and contextual semantics, an increasingly critical capability for foundation models on structured data. Empirical analysis reveals that while metadata-derived features yield modest improvements in classical prediction metrics, these metadata features consistently highlight gaps in the ability of models to leverage semantics in relational context. By reframing tabular prediction as semantics-conditioned reasoning, SALT-KG establishes a benchmark to advance tabular foundation models grounded in declarative knowledge, providing the first empirical step toward semantically linked tables in structured data at enterprise scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07638 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables Mulang, Isaiah Onando Sasaki, Felix Klein, Tassilo Kolk, Jonas Grechanov, Nikolay Hoffart, Johannes Artificial Intelligence 68T99 I.2.6; I.2.4 Building upon the SALT benchmark for relational prediction (Klein et al., 2024), we introduce SALT-KG, a benchmark for semantics-aware learning on enterprise tables. SALT-KG extends SALT by linking its multi-table transactional data with a structured Operational Business Knowledge represented in a Metadata Knowledge Graph (OBKG) that captures field-level descriptions, relational dependencies, and business object types. This extension enables evaluation of models that jointly reason over tabular evidence and contextual semantics, an increasingly critical capability for foundation models on structured data. Empirical analysis reveals that while metadata-derived features yield modest improvements in classical prediction metrics, these metadata features consistently highlight gaps in the ability of models to leverage semantics in relational context. By reframing tabular prediction as semantics-conditioned reasoning, SALT-KG establishes a benchmark to advance tabular foundation models grounded in declarative knowledge, providing the first empirical step toward semantically linked tables in structured data at enterprise scale. |
| title | SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables |
| topic | Artificial Intelligence 68T99 I.2.6; I.2.4 |
| url | https://arxiv.org/abs/2601.07638 |