文章基本信息

标题：CafeteriaFCD Corpus: Food Consumption Data Annotated with Regard to Different Food Semantic Resources
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作者：Gordana Ispirova ; Gjorgjina Cenikj ; Matevž Ogrinc 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2022
卷号：11
期号：17
页码：1-13
DOI：10.3390/foods11172684
语种：English
出版社：MDPI Publishing
摘要：Besides the numerous studies in the last decade involving food and nutrition data, this domain remains low resourced. Annotated corpuses are very useful tools for researchers and experts of the domain in question, as well as for data scientists for analysis. In this paper, we present the annotation process of food consumption data (recipes) with semantic tags from different semantic resources—Hansard taxonomy, FoodOn ontology, SNOMED CT terminology and the FoodEx2 classification system. FoodBase is an annotated corpus of food entities—recipes—which includes a curated version of 1000 instances, considered a gold standard. In this study, we use the curated version of FoodBase and two different approaches for annotating—the NCBO annotator (for the FoodOn and SNOMED CT annotations) and the semi-automatic StandFood method (for the FoodEx2 annotations). The end result is a new version of the golden standard of the FoodBase corpus, called the CafeteriaFCD (Cafeteria Food Consumption Data) corpus. This corpus contains food consumption data—recipes—annotated with semantic tags from the aforementioned four different external semantic resources. With these annotations, data interoperability is achieved between five semantic resources from different domains. This resource can be further utilized for developing and training different information extraction pipelines using state-of-the-art NLP approaches for tracing knowledge about food safety applications.
关键词：food consumption data;recipe data;annotated corpus;semantic resource;semantic tags;annotation methods;gold standard corpus