# Learning Semantic Annotations for Tabular Data

arXiv: https://arxiv.org/abs/1906.00781 · HTML: https://mlsanity.com/paper/1906.00781/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 17 of 17)

- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [PTab: Using the Pre-trained Language Model for Modeling Tabular Data](https://mlsanity.com/paper/2209.08060/) (2022)
- [TCN: Table Convolutional Network for Web Table Interpretation](https://mlsanity.com/paper/2102.09460/) (2021)
- [ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models](https://mlsanity.com/paper/2310.18208/) (2023)
- [Table Retrieval May Not Necessitate Table-specific Model Design](https://mlsanity.com/paper/2205.09843/) (2022)
- [It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings](https://mlsanity.com/paper/2203.04366/) (2022)
- [AdaTyper: Adaptive Semantic Column Type Detection](https://mlsanity.com/paper/2311.13806/) (2023)
- [Generating Table Vector Representations](https://mlsanity.com/paper/2110.15132/) (2021)
- [Graph Neural Network Approach to Semantic Type Detection in Tables](https://mlsanity.com/paper/2405.00123/) (2024)
- [KGLink: A column type annotation method that combines knowledge graph and pre-trained language model](https://mlsanity.com/paper/2406.00318/) (2024)
- [Survey on Semantic Interpretation of Tabular Data: Challenges and Directions](https://mlsanity.com/paper/2411.11891/) (2024)
- [TabEmb: Joint Semantic-Structure Embedding for Table Annotation](https://mlsanity.com/paper/2604.18939/) (2026)
- [Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)](https://mlsanity.com/paper/2107.03690/) (2021)
- [Conceptual Schema Inference for Tabular Datasets using Large Language Models](https://mlsanity.com/paper/2509.04632/) (2025)
- [ZTab: Domain-based Zero-shot Annotation for Table Columns](https://mlsanity.com/paper/2603.11436/) (2026)

## Abstract (arXiv)

The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantics, including table locality features learned by a Hybrid Neural Network (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.It exhibits good performance not only on individual table sets, but also when transferring from one table set to another.