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Food Data Normalization Using Lexical and Semantic Similarities Heuristics

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Biomedical Engineering Systems and Technologies (BIOSTEC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

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Abstract

Food is one of the main health and environmental factors in today’s society. With modernization the food supply is expanding and food-related data is increasing. This type of data comes in many different forms and making it inter-operable is one of the main requirements for using in any kind of analyses. One step towards this goal is data normalization of data coming from different sources. Food-related is collected regarding various aspects – food composition, food consumption, recipe data, etc. The most commonly encountered form is food data related to food products, which in order to serve its purpose – sales and profits, is often distorted and manipulated for marketing plans of producers and retailers. This causes the data to be often misinterpreted. There exist some studies addressing the problem of heterogeneous data by data normalization based on lexical similarity of the food products’ English names. We took this task a step further by considering data in non-English, low-resourced language – Slovenian. Working with such languages is challenging, as they have very limited resources and tools for Natural Language Processing (NLP). In our previously published work we considered different heuristics for matching food products: one based on lexical similarity [23], and two semantic similarity heuristics, i.e. based on word vector representations (embeddings). These data normalization approaches are evaluated once on a data set with 439 ground truth pairs of food products, obtained by matching their EAN barcodes. In this work, we extend this approach by introducing a new semantic similarity heuristic, based on sentence vector embeddings. Additionally, we extend the evaluation by taking real-world examples and tasking a subject-matter expert to rate the relevance of the top three matches for each example. The results show that using semantic similarity with the sentence embedding method yields best results, achieving 88% accuracy for the ground truth data set and 91% accuracy from the human expert evaluation, while the lexical similarity heuristic provides comparing results with 75% and 85% accuracy.

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Acknowledgements

This work was supported by the project from the Slovenian Research Agency (research core funding No. P2-0098), and the European Union’s Horizon 2020 research and innovation programme (grant agreements No. 863059 and No. 769661).

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 here.

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Authors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Gordana Ispirova, Gorjan Popovski, Eva Valenčič, Tome Eftimov & Barbara Koroušić Seljak

  2. Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Gordana Ispirova, Gorjan Popovski & Eva Valenčič

  3. School of Health Sciences, Faculty of Health and Medicine, Priority Research Centre in Physical Activity and Nutrition, The University of Newcastle, Callaghan, Australia

    Eva Valenčič

  4. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000, Skopje, North Macedonia

    Nina Hadzi-Kotarova

Authors
  1. Gordana Ispirova
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  2. Gorjan Popovski
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  3. Eva Valenčič
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  4. Nina Hadzi-Kotarova
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  5. Tome Eftimov
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  6. Barbara Koroušić Seljak
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Corresponding author

Correspondence to Gordana Ispirova .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Ispirova, G., Popovski, G., Valenčič, E., Hadzi-Kotarova, N., Eftimov, T., Seljak, B.K. (2021). Food Data Normalization Using Lexical and Semantic Similarities Heuristics. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72378-1

  • Online ISBN: 978-3-030-72379-8

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