CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources.

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 the health information, together with a large amount of health-related data. However, despite a 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 a European Food Safety Authority-funded project CAFETERIA, we have developed the first annotated corpus of 500 scientific abstracts that consists of 6407 annotated food entities with regard to Hansard taxonomy, 4299 for FoodOn and 3623 for SNOMED-CT. The CafeteriaSA corpus will enable the further development of natural language processing methods for food information extraction from textual data that will allow extracting food information from scientific textual data. Database URL: https://zenodo.org/record/6683798#.Y49wIezMJJF.

CafeteriaFCD Corpus: Food Consumption Data Annotated with Regard to Different Food Semantic Resources.

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.

Managing evidence in food safety and nutrition.

Evidence ('data') is at the heart of EFSA's 2020 Strategy and is addressed in three of its operational objectives: (1) adopt an open data approach, (2) improve data interoperability to facilitate data exchange, and (3) migrate towards structured scientific data. As the generation and availability of data have increased exponentially in the last decade, potentially providing a much larger evidence base for risk assessments, it is envisaged that the acquisition and management of evidence to support future food safety risk assessments will be a dominant feature of EFSA's future strategy. During the breakout session on 'Managing evidence' of EFSA's third Scientific Conference 'Science, Food, Society', current challenges and future developments were discussed in evidence management applied to food safety risk assessment, accounting for the increased volume of evidence available as well as the increased IT capabilities to access and analyse it. This paper reports on presentations given and discussions held during the session, which were centred around the following three main topics: (1) (big) data availability and (big) data connection, (2) problem formulation and (3) evidence integration.