Columns Property Annotation
4 papers with code • 3 benchmarks • 3 datasets
Column Property Annotation (CPA) refers to the task of predicting the semantic relation between two columns and is a subtask of Table Annotation. The input of a CPA problem is most commonly a pair of columns, but can also be only one column. The labels used in CPA are properties from vocabularies. Some examples are name, price, datePublished etc.
CPA is usually a multi-class classification problem and is also referred to as column relation annotation or relation extraction in different works.
T2Dv2
Most implemented papers
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.
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.
Annotating Columns with Pre-trained Language Models
Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information.
SOTAB: The WDC Schema.org Table Annotation Benchmark
This paper presents the WDC Schema. org Table Annotation Benchmark (SOTAB) for comparing the performance of table annotation systems.
