Cell Entity Annotation
6 papers with code • 5 benchmarks • 4 datasets
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.
Most implemented papers
TURL: Table Understanding through Representation Learning
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).
MAGIC: Mining an Augmented Graph using INK, starting from a CSV
A large portion of structured data does not yet reap the benefits of the Semantic Web.
JenTab Meets SemTab 2021's New Challenges
While tables are a rich source of structured information, their automated use is oftentimes prevented by the inherent ambiguity contained within.
Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching
This paper presents our contribution to the Accuracy Track of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab).
Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations
We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation.