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