Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/27288
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dc.contributor.authorIspirova, Gordanaen_US
dc.contributor.authorCenikj, Gjorgjinaen_US
dc.contributor.authorOgrinc, Matevžen_US
dc.contributor.authorValenčič, Evaen_US
dc.contributor.authorStojanov, Risteen_US
dc.contributor.authorKorošec, Peteren_US
dc.contributor.authorCavalli, Ermannoen_US
dc.contributor.authorKoroušić Seljak, Barbaraen_US
dc.contributor.authorEftimov, Tomeen_US
dc.date.accessioned2023-08-02T21:19:38Z-
dc.date.available2023-08-02T21:19:38Z-
dc.date.issued2022-09-02-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/27288-
dc.description.abstractBesides 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.en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofFoodsen_US
dc.titleCafeteriaFCD Corpus: Food Consumption Data Annotated with Regard to Different Food Semantic Resourcesen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/foods11172684-
dc.identifier.urlhttps://www.mdpi.com/2304-8158/11/17/2684/pdf-
dc.identifier.volume11-
dc.identifier.issue17-
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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