# Tab2Know: Building a Knowledge Base from Tables in Scientific Papers

arXiv: https://arxiv.org/abs/2107.13306 · HTML: https://mlsanity.com/paper/2107.13306/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Extracting Scientific Figures with Distantly Supervised Neural Networks](https://mlsanity.com/paper/1804.02445/) (2018)
- [A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents](https://mlsanity.com/paper/1804.06236/) (2018)
- [TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables](https://mlsanity.com/paper/1902.01740/) (2019)
- [Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)](https://mlsanity.com/paper/1907.00083/) (2019)
- [ColNet: Embedding the Semantics of Web Tables for Column Type Prediction](https://mlsanity.com/paper/1811.01304/) (2018)
- [WebProt'eg'e: A Cloud-Based Ontology Editor](https://mlsanity.com/paper/1902.08251/) (2019)
- [Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms](https://mlsanity.com/paper/1904.01947/) (2019)

## Built on by (most cited first, 5 of 5)

- [Evaluating Structured Decoding for Text-to-Table Generation: Evidence from Three Datasets](https://mlsanity.com/paper/2508.15910/) (2025)
- [Factual Inconsistencies in Multilingual Wikipedia Tables](https://mlsanity.com/paper/2507.18406/) (2025)
- [Data augmentation on graphs for table type classification](https://mlsanity.com/paper/2208.11210/) (2022)
- [PerOS: Personalized Self-Adapting Operating Systems in the Cloud](https://mlsanity.com/paper/2404.00057/) (2024)
- [ModelTables: A Corpus of Tables about Models](https://mlsanity.com/paper/2512.16106/) (2025)

## Abstract (arXiv)

Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of automatically interpreting the tables in papers and of disambiguating the entities that they contain. To solve these problems, we propose a pipeline that employs both statistical-based classifiers and logic-based reasoning. First, our pipeline applies weakly supervised classifiers to recognize the type of tables and columns, with the help of a data labeling system and an ontology specifically designed for our purpose. Then, logic-based reasoning is used to link equivalent entities (via sameAs links) in different tables. An empirical evaluation of our approach using a corpus of papers in the Computer Science domain has returned satisfactory performance. This suggests that ours is a promising step to create a large-scale KB of scientific knowledge.