This research focuses on developing advanced methods for assessing similarity between recipes by combining different sources of information and analytical approaches. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes. A web-based interface was developed to allow domain experts to validate the combined similarity results. After evaluating 318 recipe pairs, experts agreed on 255 (80%). The evaluation of expert assessments enables the estimation of which similarity aspects--lexical, semantic, or nutritional--are most influential in expert decision-making. The application of these methods has broad implications in the food industry and supports the development of personalized diets, nutrition recommendations, and automated recipe generation systems.

This study examines a pressing issue related to the loss of natural resources and biodiversity driven by the high reliance of food production on ecosystem management services. The well-being of all living species is impacted by this depletion, which represents a huge obstacle in our collaborative effort to improve environmental quality. Our research aims to explain the environmental effects of food production and raise awareness of pollution levels at various phases of this process. This research combines statistical analysis and visualization to show considerable differences in CO2eq emissions among 43 different food products. In particular, it highlights how animal-based diets have much higher emissions than their plant-based equivalents. Subsequently, the products were divided into three distinct groups: plant-based, animal-based, and refined oils and sugars. This demonstrated how well an unsupervised clustering technique separates food products according to their CO2eq emissions. Where, these findings highlight how excellent plant-based products are for the environment. The main goal of this study goes beyond simple observation since it aims to provide an example of how a comprehensive, health-conscious eating habit may live with a stable ecosystem and clean surroundings. Particularly, reductions in cane sugar production yield substantial reductions in CO2 emissions, whereas even marginal decreases in meat production result in a significant reduction in emissions. These results highlight the potential for sustainable eating habits to aid in environmental conservation and deepen our understanding of the complex interactions between dietary decisions and environmental effects.

Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved $R^{2}$ for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.

This research delves into the intricate dynamics of food pricing and fraud within European Union member countries. We analyze the complex interplay between food categories and countries, unraveling unique pricing trends and potential anomalies. By computing inflation-adjusted expected prices and sourcing real prices, we gain a deep understanding of inflation’s impact on actual food costs. Our multi-level analyses, network-based approaches, and cluster maps provide a global perspective, revealing international correlations in food pricing and fraud. The significance of our findings lies in setting the groundwork for understanding food fraud, informing strategies for fraud prevention, consumer protection, and, ultimately, food sustainability. Our work serves as a crucial resource for policymakers, economists, and consumers, emphasizing the importance of data-driven decision-making and transparency in the ever-evolving landscape of the European food market.

In today’s knowledge society, large number of information systems use many different individual schemes to represent data. Ontologies are a promising approach for formal knowledge representation and their number is growing rapidly. The semantic linking of these ontologies is a necessary prerequisite for establishing interoperability between the large number of services that structure the data with these ontologies. Consequently, the alignment of ontologies becomes a central issue when building a worldwide Semantic Web. There is a need to develop automatic or at least semi-automatic techniques to reduce the burden of manually creating and maintaining alignments. Ontologies are seen as a solution to data heterogeneity on the Web. However, the available ontologies are themselves a source of heterogeneity. On the Web, there are multiple ontologies that refer to the same domain, and with that comes the challenge of a given graph-based system using multiple ontologies whose taxonomy is different, but the semantics are the same. This can be overcome by aligning the ontologies or by finding the correspondence between their components.In this paper, we propose a method for indexing ontologies as a support to a solution for ontology alignment based on a neural network. In this process, for each semantic resource we combine the graph based representations from the RDF2vec model, together with the text representation from the BERT model in order to capture the semantic and structural features. This methodology is evaluated using the FoodOn and OntoFood ontologies, based on the Food Onto Map alignment dataset, which contains 155 unique and validly aligned resources. Using these limited resources, we managed to obtain accuracy of 74% and F1 score of 75% on the test set, which is a promising result that can be further improved in future. Furthermore, the methodology presented in this paper is both robust and ontology-agnostic. It can be applied to any ontology, regardless of the domain.

In the modern era of big data, one of the biggest challenges is to find an efficient way of extracting information from unstructured data and structuring it in a form that can be interpreted and utilized by both humans and computers. In this paper, we focus on the domain of food and nutrition by introducing a Machine Learning (ML) based Named Entity Recognition (NER) method, which is a crucial step in extracting information from unstructured textual data. To the best of our knowledge, this is the first corpus-based food NER method that has been enabled by the recently published FoodBase corpus. The method is based on Bidirectional Long Short-Term Memory (BiLSTM) in conjunction with Conditional Random Fields (CRF) and Representation Learning (RL). Our experiments show that, despite the relatively small amount of annotated data, BuTTER is able to successfully identify food entities from raw text, with the best of the proposed models achieving an average macro F1 score of 0.946.

In the modern era of data, advanced approaches for extracting information and knowledge from data are required. Moreover, the extracted information needs to be formalised to be usable by information systems. Today, there exist several resources of semantics on food waste, which is a huge environmental problem that need to be fixed as soon as possible. Yet, the problem is that the existing semantic resources are not aligned and therefore needs to be linked. Only in this way, the complementary knowledge from different resources will become of real value. In the paper, an AutoMap algorithm for automated mapping of knowledge on food waste from different semantic resources is presented. By integrating such an algorithm in a web based tool, experts and the general public can get an insight into complex knowledge that is required for inventing new solutions for the food waste valorisation.

Data normalization methodologies are extremely welcome to link extracted information from textual data to different semantic resources. These methodologies have been previously well researched especially in the biomedical domain, where health concepts were normalized and described using semantic tags. Recently, a methodology for normalizing food concepts has been proposed, based on Named-Entity Recognition methods resulting in the FoodOntoMap semantic resource. In this paper, we propose and evaluate a new architecture for linking phrases (i.e. textual name for foods) to concepts from semantic resources in the Food and Nutrition domain. We represent the food phrases (i.e. their textual name) in continuous vector space using state-of-the-art Natural Language Processing (NLP) embedding algorithms, and evaluate their proximity with respect to the annotated semantic food concepts. Additionally, indexing was incorporated to improve efficiency.The GloVe embedding with mean pooling provided best evaluation results, with maximum recall of 74% for the Snomed CT semantic dataset, which is promising result, but also opens a space for future improvement of the phrase representations, and their incorporation in this system.

Recently, as a part of an EU-funded project called REFRESH, a new web-based tool named FoodWasteEXplorer was developed. It provides an easy access to valuable data on unavoidable food waste that can be explored by researchers, industry, governmental agencies and the public to find ways of its valorization. The food waste data was manually collected and stored in a relational database. To enrich and make best use of it, we automatically transform the collected information into a new food waste ontology. The created Food Waste Ontology provides a formal description of knowledge from the food waste domain. Examples of its application are: (i) database querying based on natural language questions, and (ii) finding new or missing data from other datasets.