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Table annotation

Table annotation is the task of annotating a table with terms/concepts from knowledge graph or database schema. Table annotation is typically broken down into the following five subtasks:

  1. Cell Entity Annotation (CEA)
  2. Column Type Annotation (CTA)
  3. Column Property Annotation (CPA)
  4. Table Type Detection
  5. Row Annotation

The SemTab challenge is closely related to the Table Annotation problem. It is a yearly challenge which focuses on the first three tasks of table annotation and its purpose is to benchmark different table annotation systems.

Papers

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Showing 1–31 of 31 papers

TitleStatusHype
TABBIE: Pretrained Representations of Tabular DataCode1
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIsCode1
GitTables: A Large-Scale Corpus of Relational TablesCode1
JenTab Meets SemTab 2021's New ChallengesCode1
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language ModelsCode1
TURL: Table Understanding through Representation LearningCode1
bbw: Matching CSV to Wikidata via Meta-lookupCode1
A large-scale dataset for end-to-end table recognition in the wildCode1
Annotating Columns with Pre-trained Language ModelsCode1
Column Property Annotation using Large Language ModelsCode1
Column Type Annotation using ChatGPTCode1
Learning Semantic Annotations for Tabular DataCode0
Automatic Annotation and Evaluation of Error Types for Grammatical Error CorrectionCode0
BiodivTab: Semantic Table Annotation Benchmark Construction, Analysis, and New AdditionsCode0
ColNet: Embedding the Semantics of Web Tables for Column Type PredictionCode0
MAGIC: Mining an Augmented Graph using INK, starting from a CSVCode0
Matching web tables to DBpedia-A feature utility studyCode0
MTab: Matching Tabular Data to Knowledge Graph using Probability ModelsCode0
Sherlock: A Deep Learning Approach to Semantic Data Type DetectionCode0
SOTAB: The WDC Schema.org Table Annotation BenchmarkCode0
Synthesizing Realistic Data for Table RecognitionCode0
TCN: Table Convolutional Network for Web Table InterpretationCode0
Tough Tables: Carefully Evaluating Entity Linking for Tabular DataCode0
Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network—0
Kepler-aSI at SemTab 2021—0
A Corpus of Tables in Full-Text Biomedical Research Publications—0
Evaluating LLMs on Entity Disambiguation in Tables—0
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings—0
TorchicTab: Semantic Table Annotation with Wikidata and Language Models—0
Results of SemTab 2021—0
DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data—0
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