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Dataset results
4 results for “Food semantics”
CafeteriaSA corpus: Scientific abstracts annotated across different food semantic resources
<p>In the last decades, a great amount of work has been done in predictive modeling of issues related to human and environmental health. Resolution of issues related to healthcare is made possible by the existence of several biomedical vocabularies and standards, which play a crucial role in understanding health information, together with a large amount of health-related data. However, despite the large number of available resources and work done in the health and environmental domains, there is a lack of semantic resources that can be utilized in the food and nutrition domain, as well as their interconnections. For this purpose, in an European Food Safety Authority-funded project CAFETERIA, we have developed the first annotated corpus of 500 scientific abstracts that consists of 6,407 annotated food entities with regard to Hansard taxonomy, 4,299 for FoodOn, and 3,623 for SNOMED-CT. The CafeteriaSA corpus will enable further development of natural language processing methods for food information extraction from textual data that will allow extracting of food information from scientific textual data.</p>
CafeteriaFCD corpus: Food consumption data annotated with regard to different food semantic resources
<p>The FoodBase curated version which contains 1,000 manually evaluated recipes, annotated with the appropriate semantic tags from the Hansard Taxonomy, FoodON and SNOMED-CT.</p>
tFoodL: Larger Semantic Table Annotations Benchmark for Food Domain
<p><strong>tFoodL</strong> is the successor work of <a href="https://zenodo.org/records/10048187">tFood</a> that is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using 10 levels of a recursive hierarchy of related concepts in Wikidata.</p><p>Similar to tFood, it 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, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tFoodL</strong> contains 43,255 entity and horizontal tables, while this repository contains only the validation fold (10%) of the entire benchmark with its ground truth data (gt). </p><p>The supported tasks for semantic table annotations are: </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><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
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 two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other 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: </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. </p> <p> </p> <p> </p>
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