mlsanityData to 14 Oct 2026

KGLink: A column type annotation method that combines knowledge graph and pre-trained language model

arXiv 2406.00318 ↗First posted 1 Jun 2024cs.LG cs.CL cs.IRcited by 3

Not on the map. Why?

Abstract (arXiv)

The semantic annotation of tabular data plays a crucial role in various downstream tasks. Previous research has proposed knowledge graph (KG)-based and deep learning-based methods, each with its inherent limitations. KG-based methods encounter difficulties annotating columns when there is no match for column cells in the KG. Moreover, KG-based methods can provide multiple predictions for one column, making it challenging to determine the semantic type with the most suitable granularity for the dataset. This type granularity issue limits their scalability. On the other hand, deep learning-based methods face challenges related to the valuable context missing issue. This occurs when the information within the table is insufficient for determining the correct column type. This paper presents KGLink, a method that combines WikiData KG information with a pre-trained deep learning language model for table column annotation, effectively addressing both type granularity and valuable context missing issues. Through comprehensive experiments on widely used tabular datasets encompassing numeric and string columns with varying type granularity, we showcase the effectiveness and efficiency of KGLink. By leveraging the strengths of KGLink, we successfully surmount challenges related to type granularity and valuable context issues, establishing it as a robust solution for the semantic annotation of tabular data.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 1 Jun 2024.

Builds on

21 of 45 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  7. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  8. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  9. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  10. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,059
  11. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 1,003
  12. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 398
9 more
  1. 2022What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?cited by 150
  2. 2021TABBIE: Pretrained Representations of Tabular Datacited by 111
  3. 2019Sherlock: A Deep Learning Approach to Semantic Data Type Detectioncited by 74
  4. 2021Annotating Columns with Pre-trained Language Modelscited by 37
  5. 2019VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repositorycited by 28
  6. 2021TCN: Table Convolutional Network for Web Table Interpretationcited by 24
  7. 2022Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparationcited by 19
  8. 2019Learning Semantic Annotations for Tabular Datacited by 17
  9. 2018ColNet: Embedding the Semantics of Web Tables for Column Type Predictioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024RACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graphcited by 7
  2. 2025Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agentscited by 6
  3. 2026ZTab: Domain-based Zero-shot Annotation for Table Columnscited by 0