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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

SWRL rule set of the system.
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fig7: SWRL rule set of the system.

Mentions: The current FOKB contains four main classes, 58 subclasses, 15 object type properties and 17 subobject type properties, 12 data type properties, 1530 individuals with annotation type properties (Figure 1), and 210 semantic rules (Figure 7 or Box 1). Some of the rules are presented in Box 1. The rules consider four types of risks through OWL object properties: HAS_LACTOSE_RISK, HAS_GLUTEN_RISK, HAS_FISH_RISK, and HAS_EGG_RISK. Some of the negative effects of the ingredients gluten, lactose, egg, and fish are presented in Box 1. A gluten allergy for example will be discussed as a case study in the next section.


FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

SWRL rule set of the system.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: SWRL rule set of the system.
Mentions: The current FOKB contains four main classes, 58 subclasses, 15 object type properties and 17 subobject type properties, 12 data type properties, 1530 individuals with annotation type properties (Figure 1), and 210 semantic rules (Figure 7 or Box 1). Some of the rules are presented in Box 1. The rules consider four types of risks through OWL object properties: HAS_LACTOSE_RISK, HAS_GLUTEN_RISK, HAS_FISH_RISK, and HAS_EGG_RISK. Some of the negative effects of the ingredients gluten, lactose, egg, and fish are presented in Box 1. A gluten allergy for example will be discussed as a case study in the next section.

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