GitTables is a corpus of currently 1M relational tables extracted from CSV files in GitHub covering 96 topics. Table columns in GitTables have been annotated with more than 2K different semantic types from Schema.org and DBpedia. The column annotations consist of semantic types, hierarchical relations, range types, table domain and descriptions.
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The T2Dv2 dataset consists of 779 tables originating from the English-language subset of the WebTables corpus. 237 tables are annotated for the Table Type Detection task, 236 for the Columns Property Annotation (CPA) task and 235 for the Row Annotation task. The annotations that are used are DBpedia types, properties and entities.
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The ToughTables (2T) dataset was created for the SemTab challenge and includes 180 tables in total. The tables in this dataset can be categorized in two groups: the control (CTRL) group tables and tough (TOUGH) group tables.
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SOTAB V2 features two annotation tasks: Column Type Annotation (CTA) and Columns Property Annotation (CPA). The goal of the Column Type Annotation (CTA) task is to annotate the columns of a table using 82 types from the Schema.org vocabulary, such as telephone, Duration, Mass, or Organization. The goal of the Columns Property Annotation (CPA) task is to annotate pairs of table columns with one out of 108 Schema.org properties, such as gtin, startDate, priceValidUntil, or recipeIngredient. The benchmark consists of 45,834 tables annotated for CTA and 30,220 tables annotated for CPA originating from 55,511 different websites. The tables are split into training-, validation- and test sets for both tasks. The tables cover 17 popular Schema.org types including Product, LocalBusiness, Event, and JobPosting.
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VizNet-Sato is a dataset from the authors of Sato and is based on the VizNet dataset. The authors choose from VizNet only relational web tables with headers matching their selected 78 DBpedia semantic types. The selected tables are divided into two categories: Full tables and Multi-column only tables. The first category corresponds to 78,733 selected tables from VizNet, while the second category includes 32,265 tables which have more than one column. The tables of both categories are divided into 5 subsets to be able to conduct 5-fold cross validation: 4 subsets are used for training and the last for evaluation.
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The WikiTables-TURL dataset was constructed by the authors of TURL and is based on the WikiTable corpus, which is a large collection of Wikipedia tables. The dataset consists of 580,171 tables divided into fixed training, validation and testing splits. Additionally, the dataset contains metadata about each table, such as the table name, table caption and column headers.
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The WikipediaGS dataset was created by extracting Wikipedia tables from Wikipedia pages. It consists of 485,096 tables which were annotated with DBpedia entities for the Cell Entity Annotation (CEA) task.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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We present TNCR, a new table dataset with varying image quality collected from free open source websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes.
WDC SOTAB is a benchmark that features two annotation tasks: Column Type Annotation and Columns Property Annotation. The goal of the Column Type Annotation (CTA) task is to annotate the columns of a table with 91 Schema.org types, such as telephone, duration, Place, or Organization. The goal of the Columns Property Annotation (CPA) task is to annotate pairs of table columns with one out of 176 Schema.org properties, such as gtin13, startDate, priceValidUntil, or recipeIngredient. The benchmark consists of 59,548 tables annotated for CTA and 48,379 tables annotated for CPA originating from 74,215 different websites. The tables are split into training-, validation- and test sets for both tasks. The tables cover 17 popular Schema.org types including Product, LocalBusiness, Event, and JobPosting. The tables originate from the Schema.org Table Corpus.
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