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Sato: Contextual Semantic Type Detection in Tables
Summary: Sato detects column semantic types by using table-context signals with column values. A hybrid model fuses deep learning on table corpora, topic modeling, and structured prediction, delivering F1 0.925 (support-weighted) and 0.735 (macro), surpassing prior work.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12084
- Venue
- VLDB
- Year
- 2020
- Pagerank
- 7.9539265e-05
- Overall Rank
- 2,895 | 79.89%
- DOI
-
10.14778/3407790.3407793
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 25 of 25 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,122 |
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle |
2020 |
CIDR |
9.4905306e-05 |
| 2,513 |
Annotating Columns with Pre-trained Language Models |
2022 |
SIGMOD |
8.6155767e-05 |
| 2,585 |
Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks |
2024 |
SIGMOD |
8.4909917e-05 |
| 2,842 |
Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning |
2023 |
VLDB |
8.0366354e-05 |
| 3,001 |
SANTOS: Relationship-based Semantic Table Union Search |
2023 |
SIGMOD |
7.739698e-05 |
| 3,003 |
Chorus: Foundation Models for Unified Data Discovery and Exploration |
2024 |
VLDB |
7.7358219e-05 |
| 3,326 |
DeepJoin: Joinable Table Discovery with Pre-trained Language Models |
2023 |
VLDB |
7.2148323e-05 |
| 3,982 |
How Large Language Models Will Disrupt Data Management |
2023 |
VLDB |
6.5595332e-05 |
| 5,096 |
Auto-Transform: Learning-to-Transform by Patterns |
2020 |
VLDB |
5.6960764e-05 |
| 5,098 |
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models |
2024 |
VLDB |
5.6943033e-05 |
| 5,974 |
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond |
2021 |
SIGMOD |
5.2458154e-05 |
| 6,071 |
Observatory: Characterizing Embeddings of Relational Tables |
2024 |
VLDB |
5.2231739e-05 |
| 6,268 |
MATE: Multi-Attribute Table Extraction |
2022 |
VLDB |
5.1288179e-05 |
| 7,642 |
Cross Modal Data Discovery over Structured and Unstructured Data Lakes |
2023 |
VLDB |
4.6856127e-05 |
| 7,840 |
Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes |
2021 |
SIGMOD |
4.6337164e-05 |
| 8,121 |
LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes |
2024 |
VLDB |
4.5771143e-05 |
| 8,194 |
WarpGate: A Semantic Join Discovery System for Cloud Data Warehouses |
2023 |
CIDR |
4.5574831e-05 |
| 8,576 |
RECA: Related Tables Enhanced Column Semantic Type Annotation Framework |
2023 |
VLDB |
4.4879383e-05 |
| 8,732 |
Unveiling Challenges for LLMs in Enterprise Data Engineering |
2026 |
VLDB |
4.4520434e-05 |
| 8,852 |
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation |
2023 |
SIGMOD |
4.4313992e-05 |
| 9,355 |
GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example |
2023 |
SIGMOD |
4.3484264e-05 |
| 10,109 |
Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,142 |
AutoDDG: Automated Dataset Description Generation using Large Language Models |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,759 |
Cents: A Flexible and Cost-Effective Framework for LLM-Based Table Understanding |
2025 |
VLDB |
4.1905499e-05 |
| 11,207 |
Steered Training Data Generation for Learned Semantic Type Detection |
2023 |
SIGMOD |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 2,513 |
Annotating Columns with Pre-trained Language Models |
2022 |
SIGMOD |
8.6155767e-05 |
| 3,326 |
DeepJoin: Joinable Table Discovery with Pre-trained Language Models |
2023 |
VLDB |
7.2148323e-05 |
| 6,411 |
Synthesizing Type-Detection Logic for Rich Semantic Data Types using Open-source Code |
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Recovering Semantics of Tables on the Web |
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Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations |
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SIGMOD |
4.1905499e-05 |
| 5,098 |
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models |
2024 |
VLDB |
5.6943033e-05 |
| 8,915 |
Making Table Understanding Work in Practice |
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CIDR |
4.4229886e-05 |
| 3,001 |
SANTOS: Relationship-based Semantic Table Union Search |
2023 |
SIGMOD |
7.739698e-05 |
| 10,521 |
Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables |
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SIGMOD |
4.1905499e-05 |
| 8,576 |
RECA: Related Tables Enhanced Column Semantic Type Annotation Framework |
2023 |
VLDB |
4.4879383e-05 |