# TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity

arXiv: https://arxiv.org/abs/2008.10856 · HTML: https://mlsanity.com/paper/2008.10856/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

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

- [HYTREL: Hypergraph-enhanced Tabular Data Representation Learning](https://mlsanity.com/paper/2307.08623/) (2023)
- [Retrieve, Merge, Predict: Augmenting Tables with Data Lakes](https://mlsanity.com/paper/2402.06282/) (2024)
- [Enhancing Table Representations with LLM-powered Synthetic Data Generation](https://mlsanity.com/paper/2411.03356/) (2024)
- [Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting](https://mlsanity.com/paper/2502.15819/) (2025)
- [REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources](https://mlsanity.com/paper/2504.07584/) (2025)

## Abstract (arXiv)

Tables are a popular and efficient means of presenting structured information. They are used extensively in various kinds of documents including web pages. Tables display information as a two-dimensional matrix, the semantics of which is conveyed by a mixture of structure (rows, columns), headers, caption, and content. Recent research has started to consider tables as first class objects, not just as an addendum to texts, yielding interesting results for problems like table matching, table completion, or value imputation. All of these problems inherently rely on an accurate measure for the semantic similarity of two tables. We present TabSim, a novel method to compute table similarity scores using deep neural networks. Conceptually, TabSim represents a table as a learned concatenation of embeddings of its caption, its content, and its structure. Given two tables in this representation, a Siamese neural network is trained to compute a score correlating with the tables' semantic similarity. To train and evaluate our method, we created a gold standard corpus consisting of 1500 table pairs extracted from biomedical articles and manually scored regarding their degree of similarity, and adopted two other corpora originally developed for a different yet similar task. Our evaluation shows that TabSim outperforms other table similarity measures on average by app. 7% pp F1-score in a binary similarity classification setting and by app. 1.5% pp in a ranking scenario.