Table annotation

19 papers with code • 0 benchmarks • 8 datasets

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.

Most implemented papers

Sherlock: A Deep Learning Approach to Semantic Data Type Detection

mitmedialab/sherlock-project 25 May 2019

Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery.

MTab: Matching Tabular Data to Knowledge Graph using Probability Models

phucty/mtab_tool 1 Oct 2019

This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019).

Matching web tables to DBpedia-A feature utility study

T2KFramework/T2K EDBT 2017

This paper contributes to improve the understanding of the utility of different features for web table to knowledge base matching by reimplementing different matching techniques as well as similarity score aggregation methods from literature within a single matching framework and evaluating different combinations of these techniques against a single gold standard.

Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction

chrisjbryant/errant ACL 2017

Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated.

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

alan-turing-institute/SemAIDA 4 Nov 2018

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables.

Learning Semantic Annotations for Tabular Data

alan-turing-institute/SemAIDA 30 May 2019

The usefulness of tabular data such as web tables critically depends on understanding their semantics.

TURL: Table Understanding through Representation Learning

sunlab-osu/TURL 26 Jun 2020

In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables.

Tough Tables: Carefully Evaluating Entity Linking for Tabular Data

vcutrona/tough-tables International Semantic Web Conference (ISWC) 2020

Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables).

TCN: Table Convolutional Network for Web Table Interpretation

2023-MindSpore-1/ms-code-217 17 Feb 2021

Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.

bbw: Matching CSV to Wikidata via Meta-lookup

UB-Mannheim/bbw 1 Mar 2021

We present our publicly available semantic annotator bbw (boosted by wiki) tested at the second Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab2020).