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:
- 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.
Benchmarks
These leaderboards are used to track progress in Table annotation
Datasets
T2Dv2
GitTables
TNCR Dataset
Subtasks
Most implemented papers
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
mitmedialab/sherlock-project
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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
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
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
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
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
The usefulness of tabular data such as web tables critically depends on understanding their semantics.
TURL: Table Understanding through Representation Learning
sunlab-osu/TURL
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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
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
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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
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).
