| --- |
| pretty_name: SALT-KG |
| license: cc-by-nc-sa-4.0 |
| knowledge_graph: |
| path: data/salt-kg/salt-kg.json |
| format: json |
| description: Canonical full Operational Business Knowledge Graph metadata file to use programmatically. |
| viewer_derivatives: |
| - path: data/salt-kg/salt-kg-summary.parquet |
| purpose: Generated Hugging Face Dataset Viewer summary file only. Not the canonical metadata source. |
| - path: data/salt-kg/salt-kg-fields.parquet |
| purpose: Generated Hugging Face Dataset Viewer field-level display file only. Not the canonical metadata source. |
| configs: |
| - config_name: salt_kg_summary |
| default: true |
| data_files: |
| - split: metadata |
| path: data/salt-kg/salt-kg-summary.parquet |
| - config_name: salt_kg_fields |
| data_files: |
| - split: metadata |
| path: data/salt-kg/salt-kg-fields.parquet |
| - config_name: sales_document |
| data_files: |
| - split: full |
| path: data/salt/I_SalesDocument.parquet |
| - split: train |
| path: data/salt/I_SalesDocument_train.parquet |
| - split: test |
| path: data/salt/I_SalesDocument_test.parquet |
| - config_name: sales_document_item |
| data_files: |
| - split: full |
| path: data/salt/I_SalesDocumentItem.parquet |
| - split: train |
| path: data/salt/I_SalesDocumentItem_train.parquet |
| - split: test |
| path: data/salt/I_SalesDocumentItem_test.parquet |
| - config_name: joined_tables |
| data_files: |
| - split: train |
| path: data/salt/JoinedTables_train.parquet |
| - split: test |
| path: data/salt/JoinedTables_test.parquet |
| - config_name: customer |
| data_files: |
| - split: full |
| path: data/salt/I_Customer.parquet |
| - config_name: address_org_name_postal_address |
| data_files: |
| - split: full |
| path: data/salt/I_AddrOrgNamePostalAddress.parquet |
| --- |
| |
| # SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables |
|
|
| [](https://www.python.org/) |
| [](licence) |
| [](https://openreview.net/forum?id=9vVMSvilGX) |
| [](https://api.reuse.software/info/github.com/SAP-samples/salt-kg) |
|
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|
|
| ## Description |
|
|
| This repository contains the dataset from our paper SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables, presented at EurIPS'25 Table Representation Workshop. |
|
|
| The canonical metadata file for SALT-KG is `data/salt-kg/salt-kg.json`. If you want to use the full Operational Business Knowledge Graph metadata, use this file directly. |
|
|
| The files `data/salt-kg/salt-kg-summary.parquet` and `data/salt-kg/salt-kg-fields.parquet` are generated helper files for the Hugging Face Dataset Viewer only. They are provided to make the metadata browsable on Hugging Face and are not replacements for `data/salt-kg/salt-kg.json`. |
|
|
| The parquet files under `data/salt/` contain the relational SALT tables that remain available in the Dataset Viewer as separate table configurations. |
|
|
| ## Abstract |
| Building upon the SALT benchmark for relational prediction, 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 models’ ability to leverage semantics in relational context. By reframing tabular prediction as semantics-conditioned reasoning, SALT-KG establishes a benchmark to advance tabular FMs grounded in declarative knowledge, providing the first empirical step toward semantically linked tables in structured data at enterprise scale. |
|
|
| ## Why SALT-KG |
|  |
|
|
| There is growing research on Tabular Foundation Models. |
| TRL models are typically trained and evaluated on benchmarks that represent relational structure but lack explicit semantic grounding or declarative context. |
| Knowledge graph (KG) and data integration communities have explored connecting tables to semantic graphs through systems such as JENTAB. |
| We can bridge this gap by enriching enterprise relational data with an explicit semantic layer that links tables, fields, and business objects through declarative knowledge in KG |
|
|
| ## How was SALT-KG Created |
|  |
| For every relation (Table) in the underlying SALT dataset, we find a matching node in the KG (a View).. We extract triples related to the Views that include: |
| Fields: data abstraction nodes with associated fields, labels, associations, data classes, reference fields, and other elements. |
| ObjectNodeTypes: further semantic metadata through technical definitions, business object descriptions |
|
|
| ## Dataset Overview |
|  |
| The SALT-KG dataset consists of 4 tables from the SALT benchmark, enriched with semantic metadata from an Operational Business Knowledge Graph (OBKG). The dataset includes: |
| - 4 relational tables with transactional data |
| - Metadata Knowledge Graph (OBKG) with field-level descriptions, relational dependencies, and business object types |
| - Canonical metadata JSON at `data/salt-kg/salt-kg.json` |
| - Generated Hugging Face viewer helper files at `data/salt-kg/salt-kg-summary.parquet` and `data/salt-kg/salt-kg-fields.parquet` |
| - Train and test splits for the benchmark tables where provided, plus full-table parquet files for direct browsing |
|
|
| ## Requirements |
| N/A |
|
|
| ## Known Issues |
| No known issues |
|
|
| ## Authors |
| - [Isaiah Onando Mulang'](https://www.linkedin.com/in/mulang-onando-phd-31a16ab1/) |
| - [Felix Sasaki](https://www.linkedin.com/in/felixsasaki/) |
| - [Tassilo Klein](https://tjklein.github.io/) |
| - [Jonas Kolk](https://www.linkedin.com/in/jonas-kolk-b8a94b123/) |
| - [Nikolay Grechanov](https://www.linkedin.com/in/grechanov/) |
| - [Johannes Hoffart](https://www.linkedin.com/in/johanneshoffart/) |
|
|
| ## Citations |
| If you use this dataset in your research, please cite the following paper: |
|
|
| ``` |
| @inproceedings{mulang2025saltkg, |
| title={SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables}, |
| author={Mulang', Isaiah Onando and Sasaki, Felix and Klein, Tassilo and Kolk, Jonas and Grechanov, Nikolay and Hoffart, Johannes}, |
| booktitle={Proceedings of the AI for Tabular Data workshop at EurIPS 2025}, |
| year={2025} |
| } |
| ``` |
|
|
| ## How to obtain support |
| [Create an issue](https://github.com/SAP-samples/<repository-name>/issues) in this repository if you find a bug or have questions about the content. |
| |
| For additional support, [ask a question in SAP Community](https://answers.sap.com/questions/ask.html). |
|
|
| ## Contributing |
| If you wish to contribute code, offer fixes or improvements, please send a pull request. Due to legal reasons, contributors will be asked to accept a DCO when they create the first pull request to this project. This happens in an automated fashion during the submission process. SAP uses [the standard DCO text of the Linux Foundation](https://developercertificate.org/). |
|
|
| ## License |
| Copyright (c) 2025 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the CC-BY-NC-SA-4.0 except as noted otherwise in the [LICENSE](LICENSE) file. |
|
|
| SAP expressly reserves its rights against text and data mining for commercial purposes as described in [TDM_RESERVATION.txt](TDM_RESERVATION.txt). |
|
|
