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FoodViz: Visualization of Food Entities Linked Across Different Standards

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12566)

Abstract

Many research questions from different domains involve combining different data sets in order to explore a research hypothesis. One of the main problems that arises here is that different data sets are structured with respect to different domain standards and ensuring their interoperability is a time-consuming task. In the biomedical domain, the Unified Medical Language System supports interoperability between biomedical data sets by providing semantic resources and Natural Language Processing tools for automatic annotation. This allows users also to understand the links between different biomedical standards. While there are extensive resources available for the biomedical domain, the food and nutrition domain is relatively low-resourced. To make the links between different food standards understandable by food subject matter experts we propose the FoodViz. It is a web-based framework used to present food annotation results from existing Natural Language Processing and Machine Learning pipelines in combination with different food semantic data models. Using this framework, users would become more familiar with the links between different food semantic data models.

Keywords

  • Visualization
  • Food named-entity recognition
  • Big data on food and nutrition

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Notes

  1. 1.

    http://foodviz.ds4food.ijs.si/fbw/#/recipes.

  2. 2.

    https://reactjs.org/.

  3. 3.

    https://flask.palletsprojects.com/en/1.1.x/.

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Acknowledgements

This work was supported by projects from the Slovenian Research Agency [research core grant number P2-0098], and the European Union’s Horizon 2020 research and innovation programme (FNS-Cloud, Food Nutrition Security) [grant agreement 863059].

The information and the views set out in this publication are those of the authors and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use that may be made of the information contained herein.

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Correspondence to Tome Eftimov .

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Stojanov, R., Popovski, G., Jofce, N., Trajanov, D., Seljak, B.K., Eftimov, T. (2020). FoodViz: Visualization of Food Entities Linked Across Different Standards. In: , et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_4

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