mlsanityData to 7 Oct 2026

MTab: Matching Tabular Data to Knowledge Graph using Probability Models

arXiv 1910.00246 ↗First posted 1 Oct 2019cs.AI cs.DBcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

9 papers.

  1. 2021TCN: Table Convolutional Network for Web Table Interpretationcited by 24
  2. 2022AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Taskscited by 8
  3. 2024Survey on Semantic Interpretation of Tabular Data: Challenges and Directionscited by 3
  4. 2023Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profilescited by 2
  5. 2024Evaluating LLMs on Entity Disambiguation in Tablescited by 2
  6. 2023Semantic Data Management in Data Lakescited by 0
  7. 2024Column Vocabulary Association (CVA): semantic interpretation of dataless tablescited by 0
  8. 2025Relationship Detection on Tabular Data Using Statistical Analysis and Large Language Modelscited by 0
  9. 2026An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessmentcited by 0