tFood: Semantic Table Annotations Benchmark for Food Domain

<p>tFood is a dataset for tabular data to knowledge graph matching. It is derived for the Food domain and has&nbsp;two types of tables.&nbsp;On the one hand, <strong>Horizontal Relational Tables</strong>&nbsp;are where&nbsp;each table&nbsp;represents a collection of entities. On the other&nbsp;hand, <strong>Entity Tables </strong>are where each of which represents a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p> <p>The supported tasks for semantic table annotations are:&nbsp;</p> <ol> <li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li> <li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li> <li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li> <li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li> </ol> <p>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.&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>