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  1. BMC Medical Informatics and Decision Making
  2. Volume 21
  3. Issue 7
  4. The ontology of fast food facts: conceptualization of nutritional fast food data for consumers and semantic web applications
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Volume 25
Volume 24
Volume 23
Volume 22
Volume 21
Issue 11
Issue 10
Issue 9
Issue 8
Issue 7
Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge
Identification of asthma control factor in clinical notes using a hybrid deep learning model
Identification of missing concepts in biomedical terminologies using sequence-based formal concept analysis
The ontology of fast food facts: conceptualization of nutritional fast food data for consumers and semantic web applications
Issue 6
Issue 5
Issue 4
Issue 3
Issue 2
Issue 1
Volume 20
Volume 19
Volume 18
Volume 17
Volume 16
Volume 15
Volume 14
Volume 13
Volume 12
Volume 11
Volume 10
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Volume 7
Volume 6
Volume 5
Volume 4
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Volume 2
Volume 1

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The ontology of fast food facts: conceptualization of nutritional fast food data for consumers and semantic web applications

Content Provider Springer Nature : BioMed Central
Author Amith, Muhammad Onye, Chidinma Ledoux, Tracey Xiong, Grace Tao, Cui
Abstract Background Fast food with its abundance and availability to consumers may have health consequences due to the high calorie intake which is a major contributor to life threatening diseases. Providing nutritional information has some impact on consumer decisions to self regulate and promote healthier diets, and thus, government regulations have mandated the publishing of nutritional content to assist consumers, including for fast food. However, fast food nutritional information is fragmented, and we realize a benefit to collate nutritional data to synthesize knowledge for individuals. Methods We developed the ontology of fast food facts as an opportunity to standardize knowledge of fast food and link nutritional data that could be analyzed and aggregated for the information needs of consumers and experts. The ontology is based on metadata from 21 fast food establishment nutritional resources and authored in OWL2 using Protégé. Results Three evaluators reviewed the logical structure of the ontology through natural language translation of the axioms. While there is majority agreement (76.1% pairwise agreement) of the veracity of the ontology, we identified 103 out of the 430 statements that were erroneous. We revised the ontology and publicably published the initial release of the ontology. The ontology has 413 classes, 21 object properties, 13 data properties, and 494 logical axioms. Conclusion With the initial release of the ontology of fast food facts we discuss some future visions with the continued evolution of this knowledge base, and the challenges we plan to address, like the management and publication of voluminous amount of semantically linked fast food nutritional data.
Related Links https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-021-01636-1.pdf
Ending Page 15
Page Count 15
Starting Page 1
File Format HTM / HTML
ISSN 14726947
DOI 10.1186/s12911-021-01636-1
Journal BMC Medical Informatics and Decision Making
Issue Number 7
Volume Number 21
Language English
Publisher BioMed Central
Publisher Date 2021-11-09
Access Restriction Open
Subject Keyword Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Ontology Semantic web Fast food Nutrition Micropublishing
Content Type Text
Resource Type Article
Subject Health Informatics Computer Science Applications Health Policy
Journal Impact Factor 3.3/2023
5-Year Journal Impact Factor 3.9/2023
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