Abstract
The self-management of nutritional diseases requires a system that combines food tracking with the potential risks of food categories on people’s health based on their personal health records (PHRs). The challenges range from the design of an effective food image classification strategy to the development of a full-fledged knowledge-based system. This maps the results of the classification strategy into semantic information that can be exploited for reasoning. However, current works mainly address the single challenges separately without their integration into a whole pipeline. In this paper, we propose a new end-to-end semantic platform where: (i) the classification strategy aims to extract food categories from food pictures; (ii) an ontology is used for detecting the risk factors of food categories for specific diseases; (iii) the Linked Open Data (LOD) Cloud is queried for extracting information concerning related diseases and comorbidities; and, (iv) information from the users’ PHRs are exploited for generating proper personal feedback. Experiments are conducted on a new publicly released dataset. Quantitative and qualitative evaluations, from two living labs, demonstrate the effectiveness and the suitability of the proposed approach.
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Notes
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The HeLiS extension is available on the HeLiS website http://w3id.org/helis.
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The mobile applications are available on the stores and they are compliant, as the whole platform, with the GDPR rules. However, since PHRs from the Trentino Healthcare Department are used, the mobile applications cannot be used by people living outside our province. For informative purposes, here the Google Play Store links: https://play.google.com/store/apps/details?id=eu.fbk.trec.saluteplus https://play.google.com/store/apps/details?id=eu.fbk.trec.lifestyle.
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The dataset, its comparison and the code are available at https://bit.ly/2Y7zSWZ.
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Donadello, I., Dragoni, M. (2019). An End-to-End Semantic Platform for Nutritional Diseases Management. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham. https://doi.org/10.1007/978-3-030-30796-7_23
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