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Scope and aim

In the last decades, a great amount of work has been done in healthcare predictive modelling. All this work is made possible by the existence of several available biomedical vocabularies and standards, which play a crucial role for understanding health information, together with a large amount of health data. However, in 2019, Lancer Planetary Health published that starting from year 2019 the focus will be on the links between food systems, human health, and the environment. Despite the large number of available resources for the health domain, there is a limited number of resources that can be utilized in the food and nutrition domain. There are only a few rule-based food named-entity recognition systems for food and nutrient concepts extraction. There are also several food ontologies that exist, each developed for a specific application scenario, with preliminary results that focus on links between these ontologies that can be used for food and nutrition data management. The BFNDMA 2020 is a continuation of the successful workshop BFNDMA 2019 (http://cs.ijs.si/bfndma/BFNDMA.html) organized at IEEE Big Data 2019 in Los Angeles, California, USA.

In this workshop, we aim to focus primarily on methodologies for big data management and analysis for food and nutrition data. BFNDMA 2020 will consider original and unpublished research articles that propose bold steps towards addressing the challenges of data management and analysis for food and nutrition data and exploring the relation between food systems and the human health and environment.




Topics of Interest related to Food and Nutrition

  • Information retrieval, information extraction, natural language processing techniques and artificial intelligence for food and nutrition science;
  • Data normalization;
  • Knowledge representation;
  • Ontologies, vocabularies and ontology design patterns, with a focus on describing the modelling process;
  • Crowdsourcing task designs that have been used and can be (re)used for building resources such as gold standards;
  • Data mining and knowledge discovery
  • Analytics, including social media, text, or structured datasets;
  • Recommendation of food, menu, or food- and nutrition-related habits;
  • Policy analysis and recommendations relevant to citizens and governments;
  • Wearable devices, quantified-self data for food, nutrition, and health.




Invited Speakers

Giulia Menichetti, Associate Research Scientist

Network Science Institute, Barabasi Lab, Northeastern University

Giulia Menichetti is an Associate Research Scientist at the Network Science Institute (Barabasi Lab, Northeastern University). She is a physicist, with a background in network modeling of biological information. She currently leads the Foodome project that aims to track the full chemical complexity of the food we consume and develop quantitative tools to unveil, at the mechanistic level, the impact of these chemicals on our health.


Title: Exploring Food Contents in Scientific Literature with FoodMine


Thanks to the many chemical and nutritional components it carries, diet critically affects human health. However, the currently available comprehensive databases on food composition cover only a tiny fraction of the total number of chemicals present in our food, focusing on the nutritional components essential for our health. Indeed, thousands of other molecules, many of which have well documented health implications, remain untracked. To explore the body of knowledge available on food composition, we built FoodMine, a pipeline that uses natural language processing to identify papers from PubMed that potentially report on the chemical composition of garlic and cocoa. After extracting from each paper information on the reported quantities of chemicals, we find that the scientific literature carries extensive information on the detailed chemical components of food that is currently not integrated in databases. Finally, we use unsupervised machine learning to create chemical embeddings, finding that the chemicals identified by FoodMine tend to have direct health relevance, reflecting the scientific community’s focus on health-related chemicals in our food.

Dragi Kocev, PhD

Jožef Stefan Institute, Slovenia

Dragi Kocev is a senior researcher at the Department of Knowledge Technologies, JSI and the CEO and co-founder of Bias Variance Labs. He completed his PhD in 2011 at the JSI Postgraduate School in Ljubljana on the topic of learning ensemble models for predicting structured outputs. He was a visiting research fellow at the University of Bari, Italy in 2014/2015. He has participated in several national Slovenian projects, the EU funded projects IQ, PHAGOSYS and HBP. He was co-coordinator of the FP7 FET Open project MAESTRA. He is currently the principal investigator of two ESA funded projects: GALAXAI – Machine learning for spacecraft operation and AiTLAS – AI prototyping environment for EO. He has been member of the PC of premium AI/ML conferences (e.g., DS, ECML PKDD, AAAI, IJCAI, KDD) and member of the editorial board of Data Mining and Knowledge Discovery, and Ecological Informatics. He served as PC co-chair for DS 2014 and Journal track co-chair for ECML PKDD 2017.


Title: Semantic Annotation of Recipes to Investigate COVID-19 Impact on Food Consumption Process


Understanding the content of self-reported meals and online-published recipes is a basic requirement for further linking food and dietary concepts to heterogeneous health networks. Despite the huge amount of work that is done in the biomedical domain, the food and nutrition domains are relatively low-resourced. DietHub represents a step forward in food science & technology that requires knowledge from a broad spectrum of areas. DietHub is an AI workflow methodology that annotates online-published recipes or self-reported meals with the food concepts that are mentioned in them. The food semantic labels that are used are hierarchical food semantic tags from the Hansard taxonomy. We evaluated DietHub by applying it on a corpus of online-published recipes of different styles, such as health, cooking and region. The results show justifiable comparison of Mediterranean diet recipes with recipes from other diets. Addiitonaly, Using the DietHub, the changes in the food consumption patterns before and during the COVID-19 pandemic are obvious. The highest positive difference in the food consumption can be found in foods such as “Pulses/ plants producing pulses”, “Pancake/Tortilla/Outcake”, and “Soup/pottage”, which increase by 300%, 280%, and 100%, respectively. Conversely, the largest decrease in consumption can be food for food such as “Order Perciformes (type of fish)”, “Corn/cereals/grain”, and “Wine-making”, with a reduction of 50%, 40%, and 30%, respectively. This kind of analysis is valuable in times of crisis and emergencies, which is a very good example of the scientific support that regulators require in order to take quick and appropriate response.




Submission

    Please submit a full-length paper (up to 10 page IEEE 2-column format), or short or position paper (2-4 page IEEE 2-column format), through the online submission system.
    Paper Submission Page Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines
    (see link to "formatting instructions" below).

    Formatting instructions
    Microsoft Word
    LaTex Formatting Macros


    In order to participate to this workshop, full or student registration of IEEE BigData 2020 is needed.




Important dates

  • Paper submission: Oct 20, 2020
  • Decision notification: Nov 1, 2020
  • Camera-ready submission: Nov 25, 2020
  • Workshop: Dec 10-13, 2020



Organizers

Tome Eftimov, PhD

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Gorjan Popovski

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Panče Panov, PhD

Department of Knowledge Technologies

Jožef Stefan Institute, Ljubljana, Slovenia

Prof. Barbara Koroušić Seljak, PhD

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia




Duccio Cavalieri, University of Florence, IT
Gjorgjina Cenikj, Jožef Stefan Institute, SI
Hristijan Gjoreski, Ss. Cyril and Methodius University, MK
Larisa Soldatova, Goldsmiths College, UK
Martin Gjoreski, Jožef Stefan Institute, SI
Dragi Kocev, Jožef Stefan Institute, SI
Fabio Mainardi, Nestle Institute of Health Science S.A, Lausanne, CH
Bibek Paudel, Stanford University, CA, USA
Petar Ristoski, IBM Research, CA, USA
Peter Korošec, Jožef Stefan Institute, SI
Riste Stojanov, Ss. Cyril and Methodius University, MK
Monika Simjanoska, Ss. Cyril and Methodius University, MK
Maria Traka, Quadram Institute Bioscience, UK
Eva Tuba, Singidunum University, SRB
Ivona Vasileska, University of Ljubljana, SI



Workshop on Big Food and Nutrition Data Management and Analysis
Friday, December 11, 9:00AM-1:30PM (ET), Chair: Gorjan Popovski, Tome Eftimov, Panče Panov and Barbara Koroušić Seljak
9:00AM-9:30AM Invited talk: Exploring Food Contents in Scientific Literature with FoodMine (Giulia Menichetti)
9:30AM-9:50AM BuTTER: BidirecTional LSTM for Food Named-Entity Recognition (Gjorgjina Cenikj)
9:50AM-10:10AM Discovering Entity Profiles Candidate for Entity Resolution on Linked Open Data Halal Food Products (Nur Aini Rakhmawati and Ahmad Choirun Najib)
10:10AM-10:30AM Leveraging Social Network Analysis to Explore Obesity Talks on Twitter (Edwin Mitei and Thanaa Ghanem)
10:30AM-10:50AM Coffee Break
10:50AM-11:20AM Invited talk: Semantic Annotation of Recipes to Investigate COVID-19 Impact on Food Consumption Process (Dragi Kocev)
11:20AM-11:50AM Comparison of Feature Selection Algorithms for Minimization of Target Specific FFQs (Nina Rešič)
11:50AM-12:10PM Exploring Knowledge Domain Bias on a Prediction Task for Food and Nutrition Data (Gordana Ispirova)
12:10PM-12:30PM Coffee Break
12:30PM-12:50PM Toward Robust Food Ontology Mapping (Gorjan Popovski)
12:50PM-13:10PM APRICOT: A humAn-comPuteR InteraCtion tool for linking foOd wasTe streams across different (Riste Stojanov)