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Cell Entity Annotation

6 papers with code · 5 benchmarks · 4 datasets archive 2025-07-28

Knowledge Base Natural Language Processing

Cell Entity Annotation (CEA) is the task of annotating cells in a table with an entity from a knowledge base and is a subtask of Table Annotation. CEA problem labels are entities from knowledge bases such as DBpedia or WikiData. It usually is considered as a multi-class classification problem.

CEA can also be referred to in different works as the problem of entity linking, as it links a cell in a table to an entity.

Description from the archive archive 2025-07-28; Papers-with-Code links inside it are rewritten to this site.

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Dataset	Best model (first row in archive order)	Paper	Code	Syntology	Compare
BiodivTab (5 rows)	KGCODE-Tab	KGCODE-Tab Results for SemTab 2022	—	—	Compare
ToughTables-DBP (5 rows)	DAGOBAH	DAGOBAH: Table and Graph Contexts for Eﬀicient Semantic Annotation...	—	—	Compare
ToughTables-WD (5 rows)	DAGOBAH	From Heuristics to Language Models: A Journey Through the Universe...	—	—	Compare
WikipediaGS (1 row)	TURL	TURL: Table Understanding through Representation Learning	code	Syntology ran 1 of 1 samples · 0 unverified	Compare
WikiTables-TURL-CEA (1 row)	TURL	TURL: Table Understanding through Representation Learning	code	Syntology ran 1 of 1 samples · 0 unverified	Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Tough Tables BiodivTab WikiTables-TURL WikipediaGS

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Table annotation

Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (13 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations

27 Oct 2023 1 repository listed

We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation.
Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching

25 Oct 2022 1 repository listed

This paper presents our contribution to the Accuracy Track of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab).
MAGIC: Mining an Augmented Graph using INK, starting from a CSV

1 Oct 2021 1 repository listed

A large portion of structured data does not yet reap the benefits of the Semantic Web.
JenTab Meets SemTab 2021's New Challenges

1 Oct 2021 1 repository listed

While tables are a rich source of structured information, their automated use is oftentimes prevented by the inherent ambiguity contained within.
Tough Tables: Carefully Evaluating Entity Linking for Tabular Data

1 Nov 2020 1 repository listed

Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables).
TURL: Table Understanding through Representation Learning

26 Jun 2020 1 repository listed Syntology ran 1 of 1 samples · 0 unverified

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

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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