# Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles

arXiv: https://arxiv.org/abs/2302.01150 · HTML: https://mlsanity.com/paper/2302.01150/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Web Table Extraction, Retrieval and Augmentation: A Survey](https://mlsanity.com/paper/2002.00207/) (2020)
- [Novel Entity Discovery from Web Tables](https://mlsanity.com/paper/2002.00206/) (2020)
- [MTab: Matching Tabular Data to Knowledge Graph using Probability Models](https://mlsanity.com/paper/1910.00246/) (2019)
- [Learning the Semantics of Structured Data Sources](https://mlsanity.com/paper/1601.04105/) (2016)
- [Simple-ML: Towards a Framework for Semantic Data Analytics Workflows](https://mlsanity.com/paper/1909.05859/) (2019)
- [A Scalable Framework for Quality Assessment of RDF Datasets](https://mlsanity.com/paper/2001.11100/) (2020)

## Built on by (most cited first, 2 of 2)

- [Semantic Data Management in Data Lakes](https://mlsanity.com/paper/2310.15373/) (2023)
- [Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction](https://mlsanity.com/paper/2409.14192/) (2024)

## Abstract (arXiv)

Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG - a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.