tFood is a dataset for tabular data to knowledge graph matching. It is derived for the Food domain and has two types of tables. On the one hand, Horizontal Relational Tables are where each table represents a collection of entities. On the other hand, Entity Tables are where each of which represents a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG). The supported tasks for semantic table annotations are: Topic Detection (TD) links the entire table to an entity or a class from the target KG. Cell Entity Annotation (CEA) maps individual table cells to entities from the target KG. Column Type Annotation (CTA) links individual table columns to classes from the target KG. Column Property Annotation (CPA) detects the relations between column pairs from the target knowledge graph. This dataset version will be used during SemTab 2023 - Round 1. So, the ground truth data for the test set is currently hidden. We will add such ground truth after the conclusion of the challenge.

Ground truth for testing data (Entity and Horizontal) tables have been added.