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FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

Bottom Line: An ontology-driven safe food consumption mobile system is considered.Next-generation smart knowledgebase systems will not only include traditional syntactic-based search, which limits the utility of the search results, but will also provide semantics for rich searching.In this paper, performance of concept matching of food ingredients is semantic-based, meaning that it runs its own semantic based rule set to infer meaningful results through the proposed Ontology-Driven Mobile Safe Food Consumption System (FoodWiki).

View Article: PubMed Central - PubMed

Affiliation: Computer Engineering Department, Istanbul Aydin University, 34295 Istanbul, Turkey.

ABSTRACT
An ontology-driven safe food consumption mobile system is considered. Over 3,000 compounds are being added to processed food, with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emulsify, and so forth. According to World Health Organization, governments have lately focused on legislation to reduce such ingredients or compounds in manufactured foods as they may have side effects causing health risks such as heart disease, cancer, diabetes, allergens, and obesity. By supervising what and how much to eat as well as what not to eat, we can maximize a patient's life quality through avoidance of unhealthy ingredients. Smart e-health systems with powerful knowledge bases can provide suggestions of appropriate foods to individuals. Next-generation smart knowledgebase systems will not only include traditional syntactic-based search, which limits the utility of the search results, but will also provide semantics for rich searching. In this paper, performance of concept matching of food ingredients is semantic-based, meaning that it runs its own semantic based rule set to infer meaningful results through the proposed Ontology-Driven Mobile Safe Food Consumption System (FoodWiki).

No MeSH data available.


Related in: MedlinePlus

CME calculations of intolerance score for matching harm potential and Product 2.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig6: CME calculations of intolerance score for matching harm potential and Product 2.

Mentions: Figure 6 presents the steps for calculation of the intolerance score by matching Consumer and Product 2 (by using the equations in the figure). Product 2 is found to be more harmful for the Consumer since it has the higher intolerance score after concept matching.


FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

CME calculations of intolerance score for matching harm potential and Product 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4496660&req=5

fig6: CME calculations of intolerance score for matching harm potential and Product 2.
Mentions: Figure 6 presents the steps for calculation of the intolerance score by matching Consumer and Product 2 (by using the equations in the figure). Product 2 is found to be more harmful for the Consumer since it has the higher intolerance score after concept matching.

Bottom Line: An ontology-driven safe food consumption mobile system is considered.Next-generation smart knowledgebase systems will not only include traditional syntactic-based search, which limits the utility of the search results, but will also provide semantics for rich searching.In this paper, performance of concept matching of food ingredients is semantic-based, meaning that it runs its own semantic based rule set to infer meaningful results through the proposed Ontology-Driven Mobile Safe Food Consumption System (FoodWiki).

View Article: PubMed Central - PubMed

Affiliation: Computer Engineering Department, Istanbul Aydin University, 34295 Istanbul, Turkey.

ABSTRACT
An ontology-driven safe food consumption mobile system is considered. Over 3,000 compounds are being added to processed food, with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emulsify, and so forth. According to World Health Organization, governments have lately focused on legislation to reduce such ingredients or compounds in manufactured foods as they may have side effects causing health risks such as heart disease, cancer, diabetes, allergens, and obesity. By supervising what and how much to eat as well as what not to eat, we can maximize a patient's life quality through avoidance of unhealthy ingredients. Smart e-health systems with powerful knowledge bases can provide suggestions of appropriate foods to individuals. Next-generation smart knowledgebase systems will not only include traditional syntactic-based search, which limits the utility of the search results, but will also provide semantics for rich searching. In this paper, performance of concept matching of food ingredients is semantic-based, meaning that it runs its own semantic based rule set to infer meaningful results through the proposed Ontology-Driven Mobile Safe Food Consumption System (FoodWiki).

No MeSH data available.


Related in: MedlinePlus