dorsal/arxiv
View SchemaSALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables
| Authors | Isaiah Onando Mulang, Felix Sasaki, Tassilo Klein, Jonas Kolk, Nikolay Grechanov, Johannes Hoffart |
|---|---|
| Categories | |
| ArXiv ID | 2601.07638vv1 |
| URL | https://arxiv.org/abs/2601.07638 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
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.
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"abstract": "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.",
"arxiv_id": "2601.07638",
"authors": [
"Isaiah Onando Mulang",
"Felix Sasaki",
"Tassilo Klein",
"Jonas Kolk",
"Nikolay Grechanov",
"Johannes Hoffart"
],
"categories": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables",
"url": "https://arxiv.org/abs/2601.07638",
"version": "v1"
},
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"type": "Model",
"variant": "snapshot-2026-01-17",
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