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Table annotation

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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:

Cell Entity Annotation (CEA)
Column Type Annotation (CTA)
Column Property Annotation (CPA)
Table Type Detection
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.

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Evaluating Knowledge Generation and Self-Refinement Strategies for LLM-based Column Type Annotation

ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations

RACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graph

Evaluating LLMs on Entity Disambiguation in Tables

Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs

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

Synthesizing Realistic Data for Table Recognition

TableLlama: Towards Open Large Generalist Models for Tables

ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models

Adversarial Attacks on Tables with Entity Swap

CHORUS: Foundation Models for Unified Data Discovery and Exploration

Column Type Annotation using ChatGPT

A large-scale dataset for end-to-end table recognition in the wild

GitTables: A Large-Scale Corpus of Relational Tables

TABBIE: Pretrained Representations of Tabular Data

Annotating Columns with Pre-trained Language Models

TCN: Table Convolutional Network for Web Table Interpretation

Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers

AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model

TURL: Table Understanding through Representation Learning

Sato: Contextual Semantic Type Detection in Tables

MTab: Matching Tabular Data to Knowledge Graph using Probability Models

Learning Semantic Annotations for Tabular Data

Sherlock: A Deep Learning Approach to Semantic Data Type Detection

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.