# MTab: Matching Tabular Data to Knowledge Graph using Probability Models

arXiv: https://arxiv.org/abs/1910.00246 · HTML: https://mlsanity.com/paper/1910.00246/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [TCN: Table Convolutional Network for Web Table Interpretation](https://mlsanity.com/paper/2102.09460/) (2021)
- [AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks](https://mlsanity.com/paper/2209.00946/) (2022)
- [Survey on Semantic Interpretation of Tabular Data: Challenges and Directions](https://mlsanity.com/paper/2411.11891/) (2024)
- [Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles](https://mlsanity.com/paper/2302.01150/) (2023)
- [Evaluating LLMs on Entity Disambiguation in Tables](https://mlsanity.com/paper/2408.06423/) (2024)
- [Relationship Detection on Tabular Data Using Statistical Analysis and Large Language Models](https://mlsanity.com/paper/2506.06371/) (2025)
- [Semantic Data Management in Data Lakes](https://mlsanity.com/paper/2310.15373/) (2023)
- [Column Vocabulary Association (CVA): semantic interpretation of dataless tables](https://mlsanity.com/paper/2409.13709/) (2024)
- [An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment](https://mlsanity.com/paper/2610.10541/) (2026)

## Abstract (arXiv)

This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019). MTab combines the voting algorithm and the probability models to solve critical problems of the matching tasks. Results on SemTab 2019 show that MTab obtains promising performance for the three matching tasks.