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FoodOntoMapV2: Food Concepts Normalization Across Food Ontologies

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1297))

Abstract

Nowadays, the existence of several available biomedical vocabularies and standards play a crucial role in understanding health information. While there is a large number of available resources in the biomedical domain, only a limited number of resources can be utilized in the food domain. There are only a few annotated corpora with food concepts, as well as a small number of rule-based food named-entity recognition systems for food concept extraction. Additionally, several food ontologies exist, each developed for a specific application scenario. To address the issue of ontology alignment, we have previously created a resource, named FoodOntoMap, that consists of food concepts extracted from recipes. The extracted concepts were annotated by using semantic tags from four different food ontologies. To make the resource more comprehensive, as well as more representative of the domain, in this paper we have extended this resource by creating a second version, appropriately named FoodOntoMapV2. This was done by including an additional four ontologies that contain food concepts. Moreover, this resource can be used for normalizing food concepts across ontologies and developing applications for understanding the relation between food systems, human health, and the environment.

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References

  1. Alexander, M., Anderson, J.: The hansard corpus 1803–2003 (2012)

    Google Scholar 

  2. Association, A.M., et al.: CPT, 1989: Physician’s Current Procedural Terminology. American Medical Association Press (1989)

    Google Scholar 

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

    Article  Google Scholar 

  4. 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 

  5. Caracciolo, C., Stellato, A., Rajbahndari, S., Morshed, A., Johannsen, G., Jaques, Y., Keizer, J.: Thesaurus maintenance, alignment and publication as linked data: the AGROVOC use case. Int. J. Metadata Semant. Ontol. 7(1), 65–75 (2012)

    Article  Google Scholar 

  6. Çelik, D.: Foodwiki: Ontology-driven mobile safe food consumption system. Sci. World Jo. 2015 (2015)

    Google Scholar 

  7. Chen, Y., Lasko, T.A., Mei, Q., Denny, J.C., Xu, H.: A study of active learning methods for named entity recognition in clinical text. J. Biomed. Inform. 58, 11–18 (2015)

    Article  Google Scholar 

  8. First DataBank: National drug data file (NDDF) (2008)

    Google Scholar 

  9. Díaz-Galiano, M.C., García-Cumbreras, M.Á., Martín-Valdivia, M.T., Montejo-Ráez, A., Ureña-López, L.A.: Integrating MeSH ontology to improve medical information retrieval. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 601–606. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85760-0_76

    Chapter  Google Scholar 

  10. Donnelly, K.: SNOMED-CT: The advanced terminology and coding system for eHealth. Stud. Health Technol. Inform. 121, 279 (2006)

    Google Scholar 

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

    Google Scholar 

  12. 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 

  13. 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 

  14. 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)

  15. 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 

  16. Groves, S.: How allrecipes. com became the worlds largest food/recipe site. roi of social media (blog) (2013)

    Google Scholar 

  17. 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 

  18. Kolchin, M., Zamula, D.: Food product ontology: initial implementation of a vocabulary for describing food products. In: Proceeding of the 14th Conference of Open Innovations Association FRUCT, Helsinki, Finland, pp. 11–15 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  20. Liu, S., Ma, W., Moore, R., Ganesan, V., Nelson, S.: RXNORM: prescription for electronic drug information exchange. IT Prof. 7(5), 17–23 (2005)

    Article  Google Scholar 

  21. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. Pennington, J.A., Smith, E.C., Chatfield, M.R., Hendricks, T.C.: Langual: a food-description language. Terminology. Int. J. Theoret. Appl. Issues Special. Commun. 1(2), 277–289 (1994)

    Google Scholar 

  24. Popovski, G., Ispirova, G., Hadzi-Kotarova, N., Valenčič, E., Eftimov, T., Koroušić Seljak, B.: Food data integration by using heuristics based on lexical and semantic similarities. In: Proceedings of the 13th International Conference on Health Informatics (2020, in Press)

    Google Scholar 

  25. 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 

  26. 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 - Volume 2: KEOD, pp. 195–202. INSTICC, SciTePress (2019). https://doi.org/10.5220/0008353201950202

  27. 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

  28. Quan, H., et al.: Coding algorithms for defining comorbidities in icd-9-cm and icd-10 administrative data. Medical care pp. 1130–1139 (2005)

    Google Scholar 

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

    Google Scholar 

  30. Snae, C., Brückner, M.: Foods: a food-oriented ontology-driven system. In: 2nd IEEE International Conference on Digital Ecosystems and Technologies, DEST 2008 pp. 168–176. IEEE (2008)

    Google Scholar 

  31. 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. Inform. 103133 (2019)

    Google Scholar 

  32. Wang, Y.: An empirical evaluation of semantic similarity measures using the wordnet and UMLS ontologies. Ph.D. thesis, Miami University (2005)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Ad Futura grant for postgraduate study; the Slovenian Research Agency Program P2-0098; and the European Union’s Horizon 2020 research and innovation programme [grant agreement No 863059].

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Authors and Affiliations

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

    Gorjan Popovski, Barbara Koroušić Seljak & Tome Eftimov

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

    Gorjan Popovski

Authors
  1. Gorjan Popovski
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  2. Barbara Koroušić Seljak
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  3. Tome Eftimov
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Corresponding author

Correspondence to Gorjan Popovski .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. Polytechnic Institute of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Popovski, G., Seljak, B.K., Eftimov, T. (2020). FoodOntoMapV2: Food Concepts Normalization Across Food Ontologies. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham. https://doi.org/10.1007/978-3-030-66196-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-66196-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66195-3

  • Online ISBN: 978-3-030-66196-0

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