SpringerLink
Log in

Food Composition Knowledge Extraction from Scientific Literature

  • Conference paper
  • First Online:
Artificial Intelligence: Towards Sustainable Intelligence (AI4S 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1907))

Abstract

Adequate nutrition is an essential catalyst for economic and human development as well as for achieving Sustainable Development Goals (Goal 2 and Goal 3). Thus, understanding Food Composition (stored in Food Composition Tables) can allow people to have a healthy diet and avoid overnutrition and undernutrition which are cause of lots of health problems. Food Composition Tables (FCT) or Food Composition Databases (FCD) contains the food we eat and what it contains. It is built by using chemical analysis to determine the different composition and structure of foods. However, the chemical analysis of food requires significant financial resources and skilled laboratory investigators. These resources are not always available. Given that many FCT are stored in scientific papers related to food, nutrition, food chemistry, etc. in the form of tables, we propose in this paper to extract these knowledge for the purpose of building Food Composition Tables. The latter can therefore be used for food recommendation, ingredient substitution, etc. To demonstrate the relevance of the knowledge extracted, we invited one domain expert for validation. On the other hand, we compared an excerpt of the knowledge extracted to several biomedical ontologies, food ontologies and we matched some elements extracted to FoodOn ontology and Wikidata Knowledge Graph.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/jiofidelus/tsotsa.

  2. 2.

    https://github.com/jiofidelus/tsotsa/tree/main/TSOTSATable_dataset.

  3. 3.

    https://www.semanticscholar.org/.

  4. 4.

    https://www.fao.org/infoods/infoods.

  5. 5.

    https://www.langual.org.

  6. 6.

    https://www.sciencedirect.com/search.

  7. 7.

    https://pypi.org/project/paddleocr/.

  8. 8.

    https://github.com/Neuralearn/pdf-to-excel.

  9. 9.

    https://colab.research.google.com/drive/1gOPBCVO9VtKcoIewXyr_6nNoxo1Bkqbz.

  10. 10.

    https://youtu.be/HZh31OGiQRQ.

  11. 11.

    https://developer.ibm.com/exchanges/data/all/publaynet/.

  12. 12.

    https://bioportal.bioontology.org/ontologies/FGNHNS.

  13. 13.

    https://bioportal.bioontology.org/ontologies/OF.

  14. 14.

    https://bioportal.bioontology.org/ontologies/FMPM.

References

  1. Bordea, G., Nikiema, J.N., Griffier, R., Hamon, T.: FIDEO: food interactions with drugs evidence ontology. In: ICBO/ODLS (2020)

    Google Scholar 

  2. Castellano-Escuder, P., González-Domínguez, R., Wishart, D.S., Andrés-Lacueva, C., Sánchez-Pla, A.: FOBI: an ontology to represent food intake data and associate it with metabolomic data. Database J. Biol. Databases Curation 2020 (2020)

    Google Scholar 

  3. Eftimov, T., Ispirova, G., Potovcnik, D., Ogrinc, N., Seljak, B.: ISO-FOOD ontology: a formal representation of the knowledge within the domain of isotopes for food science. Food Chem. 277, 382–390 (2019)

    Article  Google Scholar 

  4. International Network of Food Data Systems (INFOODS): Sustainable development goals (2022). https://www.fao.org/infoods/infoods/en/

  5. Greenfield, H., Southgate, D.A.: Food composition data: production, management, and use. Food & Agriculture Organization (2003). https://doi.org/10.1007/978-1-4615-3544-7

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385

  7. He, M., et al.: ICPR 2018 contest on robust reading for multi-type web images. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 7–12 (2018). https://doi.org/10.1109/ICPR.2018.8546143

  8. He, W., Zhang, X.Y., Yin, F., Liu, C.L.: Multi-oriented and multi-lingual scene text detection with direct regression. IEEE Trans. Image Process. (TIP) 27(11), 5406–5419 (2018)

    Article  MathSciNet  Google Scholar 

  9. Huang, X., et al.: PP-YOLOv2: a practical object detector. CoRR abs/2104.10419 (2021). https://arxiv.org/abs/2104.10419

  10. Huang, Z., et al.: ICDAR 2019 competition on scanned receipt OCR and information extraction. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1516–1520 (2019). https://doi.org/10.1109/ICDAR.2019.00244

  11. Informatics, D.F.: Langual - the international framework for food description (2020). https://www.langual.org/Default.asp

  12. Jiménez-Ruiz, E., Hassanzadeh, O., Efthymiou, V., Chen, J., Srinivas, K.: SemTab 2019: resources to benchmark tabular data to knowledge graph matching systems. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 514–530. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49461-2_30

    Chapter  Google Scholar 

  13. Jiomekong, A.: Comparison of food composition tables/databases (2022). https://orkg.org/comparison/R206121/

  14. Jiomekong, A.: Food information engineering: a systematic literature review. Proc. AAAI Conf. Artif. Intell. 37(13), 15441 (2023). https://doi.org/10.1609/aaai.v37i13.26808. https://ojs.aaai.org/index.php/AAAI/article/view/26808

  15. Jiomekong, A., Camara, G., Tchuente, M.: Extracting ontological knowledge from Java source code using hidden Markov models. Open Comput. Sci. 9(1), 181–199 (2019)

    Article  Google Scholar 

  16. Jiomekong, A., et al.: A large scale corpus of food composition tables. In: SemTab@ISWC (2022)

    Google Scholar 

  17. Jiomekong, A., Uriel, M., Tapamo, H., Camara, G.: Semantic annotation of TSOTSAtable dataset. In: SemTab@ISWC (2023)

    Google Scholar 

  18. Karatzas, D., Mestre, S.R., Mas, J., Nourbakhsh, F., Roy, P.P.: ICDAR 2011 robust reading competition - challenge 1: reading text in born-digital images (web and email). In: 2011 International Conference on Document Analysis and Recognition, pp. 1485–1490 (2011). https://doi.org/10.1109/ICDAR.2011.295

  19. Khalis, M., et al.: Update of the Moroccan food composition tables: towards a more reliable tool for nutrition research. J. Food Compos. Anal. 87, 103,397 (2020)

    Google Scholar 

  20. Kouebou, C., et al.: A review of composition studies of Cameroon traditional dishes: macronutrients and minerals. Food Chem. 140(3), 483–494 (2013)

    Article  Google Scholar 

  21. Li, C., et al.: PP-OCRv3: more attempts for the improvement of ultra lightweight OCR system (2022)

    Google Scholar 

  22. Makwana, Y., Iyer, S.S., Tiwari, S.: The food recognition and nutrition assessment from images using artificial intelligence: a survey. ECS Trans. 107(1), 3547 (2022)

    Article  Google Scholar 

  23. Nayef, N., et al.: ICDAR 2019 robust reading challenge on multi-lingual scene text detection and recognition - RRC-MLT-2019. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1582–1587 (2019). https://doi.org/10.1109/ICDAR.2019.00254

  24. O’Gorman, L.: The document spectrum for page layout analysis. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1162–1173 (1993). https://doi.org/10.1109/34.244677

    Article  Google Scholar 

  25. Romero, M.M., Jonquet, C., O’Connor, M.J., Graybeal, J., Pazos, A., Musen, M.A.: NCBO ontology recommender 2.0: an enhanced approach for biomedical ontology recommendation. J. Biomed. Semant. 8(1), 21:1–21:22 (2017). https://doi.org/10.1186/s13326-017-0128-y

  26. Shi, B., et al.: ICDAR 2017 competition on reading Chinese text in the wild (RCTW-17). In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 1429–1434 (2017). https://doi.org/10.1109/ICDAR.2017.233

  27. Sun, Y., Liu, J., Liu, W., Han, J., Ding, E., Liu, J.: Chinese street view text: large-scale Chinese text reading with partially supervised learning. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9085–9094. IEEE Computer Society, Los Alamitos, CA, USA (2019). https://doi.org/10.1109/ICCV.2019.00918. https://doi.ieeecomputersociety.org/10.1109/ICCV.2019.00918

  28. UN: Sustainable development goals (2015). https://sdgs.un.org/goals

  29. Usip, P.U., Udo, A., Ijebu, F.F., Tiwari, S.: A review on multilingual food recommendation systems for critical medical conditions in pregnancy care (2022)

    Google Scholar 

  30. Watanabe, T.: Food composition tables of japan and the nutrient table/database. J. Nutr. Sci. Vitaminol. 61(Supplement), S25–S27 (2015)

    Article  Google Scholar 

  31. Whetzel, P.L., et al.: BioPortal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res. 39, 541–545 (2011). https://doi.org/10.1093/nar/gkr469

    Article  Google Scholar 

  32. Xu, Z., et al.: Towards end-to-end license plate detection and recognition: a large dataset and baseline. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 261–277. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_16

    Chapter  Google Scholar 

  33. Yadav, S., Powers, M., Vakaj, E., Tiwari, S., Ortiz-Rodriguez, F., Martinez-Rodriguez, J.L.: Semantic based carbon footprint of food supply chain management. In: Proceedings of the 24th Annual International Conference on Digital Government Research, pp. 657–659 (2023)

    Google Scholar 

  34. Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1083–1090. IEEE (2012)

    Google Scholar 

  35. Zhong, X., Tang, J., Yepes, A.J.: PubLayNet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1015–1022. IEEE (2019). https://doi.org/10.1109/ICDAR.2019.00166

Download references

Acknowledgements

We are grateful to neuralearn.ai for having provided the video tutorial and all the machine learning source code needed to extract tables from scientific papers. We are also grateful to Kangsi Germain, Professor in Food Science and Nutrition for the contribution to the validation of information extracted.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Yaounde 1, Yaounde, Cameroon

    Azanzi Jiomekong & Hippolyte Tapamo

  2. neuralearn.ai, Yaounde, Cameroon

    Martins Folefac

Authors
  1. Azanzi Jiomekong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martins Folefac
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hippolyte Tapamo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Azanzi Jiomekong .

Editor information

Editors and Affiliations

  1. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  2. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India

    Sashikala Mishra

  4. Birmingham City University, Birmingham, UK

    Edlira Vakaj

  5. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India

    Ketan Kotecha

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiomekong, A., Folefac, M., Tapamo, H. (2023). Food Composition Knowledge Extraction from Scientific Literature. In: Tiwari, S., Ortiz-Rodríguez, F., Mishra, S., Vakaj, E., Kotecha, K. (eds) Artificial Intelligence: Towards Sustainable Intelligence. AI4S 2023. Communications in Computer and Information Science, vol 1907. Springer, Cham. https://doi.org/10.1007/978-3-031-47997-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47997-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47996-0

  • Online ISBN: 978-3-031-47997-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation