Skip to main content
SpringerLink
Log in

FoodViz: Visualization of Food Entities Linked Across Different Standards

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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/.

References

  1. Food in the anthropocene: the eat-lancet commission on healthy diets from sustainable food systems. Lancet 393(10170), 447–492 (2019)

    Article  Google Scholar 

  2. Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (2020). https://www.ipcc.ch/site/assets/uploads/2019/08/4.-SPM_Approved_Microsite_FINAL.pdf

  3. Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4614-3223-4

    Book  Google Scholar 

  4. Alexander, M., Anderson, J.: The hansard corpus, 1803-2003 (2012)

    Google Scholar 

  5. Alnazzawi, N., Thompson, P., Batista-Navarro, R., Ananiadou, S.: Using text mining techniques to extract phenotypic information from the phenochf corpus. BMC Med. Inform. Decis. Mak. 15(2), 1 (2015)

    Google Scholar 

  6. Arighi, C.N., et al.: Overview of the biocreative iii workshop. BMC Bioinformat. 12(8), 1 (2011)

    Article  Google Scholar 

  7. Aronson, A.R.: MetaMap: mapping text to the UMLs metathesaurus. Bethesda, MD: NLM, NIH, DHHS 1, 26 (2006)

    Google Scholar 

  8. Boag, W., Wacome, K., Naumann, T., Rumshisky, A.: Cliner: a lightweight tool for clinical named entity recognition. AMIA Joint Summits on Clinical Research Informatics (poster) (2015)

    Google Scholar 

  9. Bodenreider, O.: The unified medical language system (UMLs): integrating biomedical terminology. Nucleic Acids Res. 32(Suppl-1), D267–D270 (2004)

    Article  Google Scholar 

  10. Boulos, M.N.K., Yassine, A., Shirmohammadi, S., Namahoot, C.S., Brückner, M.: Towards an “internet of food”: Food ontologies for the internet of things. Future Internet 7(4), 372–392 (2015)

    Article  Google Scholar 

  11. Donnelly, K.: SNOMED-CT: the advanced terminology and coding system for eHealth. Stud. Health Technol. Informat. 121, 279 (2006)

    Google Scholar 

  12. (EFSA), E.F.S.A.: The food classification and description system foodex 2 (revision 2). EFSA Support. Publ. 12(5), 804E (2015)

    Google Scholar 

  13. Eftimov, T., Korošec, P., Koroušić Seljak, B.: Standfood: standardization of foods using a semi-automatic system for classifying and describing foods according to FoodEx2. Nutrients 9(6), 542 (2017)

    Article  Google Scholar 

  14. Eftimov, T., Koroušić Seljak, B., Korošec, P.: A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations. PLoS ONE 12(6), e0179488 (2017)

    Article  Google Scholar 

  15. Eftimov, T., Popovski, G., Valenčič, E., Seljak, B.K.: Foodex2vec: New foods’ representation for advanced food data analysis. Food Chem. Toxicol. 138, 111169 (2020)

    Article  Google Scholar 

  16. Gligic, L., Kormilitzin, A., Goldberg, P., Nevado-Holgado, A.: Named entity recognition in electronic health records using transfer learning bootstrapped neural networks. arXiv preprint arXiv:1901.01592 (2019)

  17. Griffiths, E.J., Dooley, D.M., Buttigieg, P.L., Hoehndorf, R., Brinkman, F.S., Hsiao, W.W.: Foodon: a global farm-to-fork food ontology. In: ICBO/BioCreative (2016)

    Google Scholar 

  18. Hanisch, D., Fundel, K., Mevissen, H.T., Zimmer, R., Fluck, J.: Prominer: rule-based protein and gene entity recognition. BMC Bioinformat. 6(1), S14 (2005)

    Article  Google Scholar 

  19. Johan, F., Owen, G.: Exponential roadmap (2020). https://exponentialroadmap.org/wp-content/uploads/2019/09/Exponential-Roadmap-1.5-September-19-2019.pdf

  20. Jonquet, C., Shah, N., Youn, C., Callendar, C., Storey, M.A., Musen, M.: NCBO annotator: semantic annotation of biomedical data. In: International Semantic Web Conference, Poster and Demo Session, vol. 110 (2009)

    Google Scholar 

  21. Kim, S., et al.: Biocreative v bioc track overview: collaborative biocurator assistant task for biogrid. Database 2016, baw121 (2016)

    Google Scholar 

  22. Lartey, A.: End hunger, achieve food security and improved nutrition and promote sustainable agriculture. UN Chronicle 51(4), 6–8 (2015)

    Article  Google Scholar 

  23. Leaman, R., Wei, C.H., Zou, C., Lu, Z.: Mining patents with tmChem, GNormPlus and an ensemble of open systems. In: Proceedings The Fifth BioCreative Challenge Evaluation Workshop, pp. 140–146 (2015)

    Google Scholar 

  24. Lopez, M.M., Kalita, J.: Deep learning applied to nlp. arXiv preprint arXiv:1703.03091 (2017)

  25. Noy, N.F., et al.: Bioportal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 37(suppl\(\_\)2), W170–W173 (2009)

    Google Scholar 

  26. Petasis, G., Vichot, F., Wolinski, F., Paliouras, G., Karkaletsis, V., Spyropoulos, C.D.: Using machine learning to maintain rule-based named-entity recognition and classification systems. In: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, pp. 426–433. Association for Computational Linguistics (2001)

    Google Scholar 

  27. Popovski, G., Kochev, S., Koroušić Seljak, B., Eftimov, T.: Foodie: a rule-based named-entity recognition method for food information extraction. In: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2019), pp. 915–922 (2019)

    Google Scholar 

  28. Popovski, G., Koroušić Seljak, B., Eftimov, T.: Foodontomap: linking food concepts across different food ontologies. In: Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, KEOD, vol. 2, pp. 195–202. INSTICC, SciTePress (2019). https://doi.org/10.5220/0008353201950202

  29. Popovski, G., Paudel, B., Eftimov, T., Seljak, B.K.: Exploring a standardized language for describing foods using embedding techniques. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5172–5176. IEEE (2019)

    Google Scholar 

  30. Popovski, G., Seljak, B.K., Eftimov, T.: A survey of named-entity recognition methods for food information extraction. IEEE Access 8, 31586–31594 (2020)

    Article  Google Scholar 

  31. Popovski, G., Seljak, B.K., Eftimov, T.: FoodBase corpus: a new resource of annotated food entities. Database 2019, November 2019. https://doi.org/10.1093/database/baz121

  32. Rastegar-Mojarad, M., et al.: Biocreative/OHNLP challenge 2018. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 575–575. ACM (2018)

    Google Scholar 

  33. Rayson, P., Archer, D., Piao, S., McEnery, A.: The UCREL semantic analysis system, (2004)

    Google Scholar 

  34. Settles, B.: Active learning literature survey. University of Wisconsin, Madison 52(55–66), 11 (2010)

    Google Scholar 

  35. Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: Glue: a multi-task benchmark and analysis platform for natural language understanding (2020). https://gluebenchmark.com

  36. Wang, Q., Zhou, Y., Ruan, T., Gao, D., Xia, Y., He, P.: Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition. J. Biomed. Informat. 103133 (2019)

    Google Scholar 

  37. Wang, Q., et al.: Overview of the interactive task in biocreative v. Database 2016, baw119 (2016)

    Google Scholar 

  38. Wei, C.H., et al.: Assessing the state of the art in biomedical relation extraction: overview of the biocreative v chemical-disease relation (CDR) task. Database 2016, baw032 (2016)

    Google Scholar 

  39. Zhou, X., Zhang, X., Hu, X.: MaxMatcher: biological concept extraction using approximate dictionary lookup. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 1145–1149. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-36668-3_150

    Chapter  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

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

    Riste Stojanov, Nasi Jofce & Dimitar Trajanov

  2. Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

    Gorjan Popovski, Barbara Koroušić Seljak & Tome Eftimov

  3. Jožef Stefan International Postgraduate School, Ljubljana, Slovenia

    Gorjan Popovski

Authors
  1. Riste Stojanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gorjan Popovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nasi Jofce
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dimitar Trajanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Barbara Koroušić Seljak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tome Eftimov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tome Eftimov .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stojanov, R., Popovski, G., Jofce, N., Trajanov, D., Seljak, B.K., Eftimov, T. (2020). FoodViz: Visualization of Food Entities Linked Across Different Standards. In: Nicosia, G., 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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation