# Survey on Semantic Interpretation of Tabular Data: Challenges and Directions

arXiv: https://arxiv.org/abs/2411.11891 · HTML: https://mlsanity.com/paper/2411.11891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 182 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Learning Semantic Annotations for Tabular Data](https://mlsanity.com/paper/1906.00781/) (2019)
- [MTab: Matching Tabular Data to Knowledge Graph using Probability Models](https://mlsanity.com/paper/1910.00246/) (2019)
- [Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia](https://mlsanity.com/paper/1812.06280/) (2018)
- [Evaluating LLMs on Entity Disambiguation in Tables](https://mlsanity.com/paper/2408.06423/) (2024)

## Built on by (most cited first, 3 of 3)

- [Relationship Detection on Tabular Data Using Statistical Analysis and Large Language Models](https://mlsanity.com/paper/2506.06371/) (2025)
- [SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge](https://mlsanity.com/paper/2506.21819/) (2025)
- [InRTL: Effective Intra-Inter Interaction Learning for Relational Tables](https://mlsanity.com/paper/2609.12712/) (2026)

## Abstract (arXiv)

Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions.