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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

Semantic enhancement of the concept matching engine (CME).
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fig3: Semantic enhancement of the concept matching engine (CME).

Mentions: Assume that the food domain specifies the term “Ascorbic Acid” as having two synonyms: “Vitamin C” or its E-codex standard name “E300” (Figure 3). In addition, “Ascorbic Acid” is marked with “hasGroup name” as “Ascorbate” and “Is_a relation” of “Antioxidant,” which is connected to another “Is_a” relation of “Food Additive” (Figure 3) (http://www.ingredientswizard.com/e-numbers-overview/320-e300e399-antioxidants-acidity-regulators-). The meaning of this then is that “Ascorbic Acid” is a “Food Additive” and also an “Antioxidant” that has two synonyms, “Vitamin C” and “E300,” and is in the “Ascorbate” group within the food domain. In addition, also assume that the concept of side effects from food nutrients related to a consumer's health problem is “Ascorbic Acid” or “E300,” which appears on a food product's label. However, the consumer knows only that vitamin C has a side effect on him or her and does not know the meaning of “Ascorbic Acid” or “E300.” A smart system needs to recognize the risk, define an intolerance score, and warn the consumer before he or she consumes the selected food product. Thus, the system considers the entire set of related concepts, properties, individuals, or synonyms according to the consumer intolerance list.


FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

Semantic enhancement of the concept matching engine (CME).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Semantic enhancement of the concept matching engine (CME).
Mentions: Assume that the food domain specifies the term “Ascorbic Acid” as having two synonyms: “Vitamin C” or its E-codex standard name “E300” (Figure 3). In addition, “Ascorbic Acid” is marked with “hasGroup name” as “Ascorbate” and “Is_a relation” of “Antioxidant,” which is connected to another “Is_a” relation of “Food Additive” (Figure 3) (http://www.ingredientswizard.com/e-numbers-overview/320-e300e399-antioxidants-acidity-regulators-). The meaning of this then is that “Ascorbic Acid” is a “Food Additive” and also an “Antioxidant” that has two synonyms, “Vitamin C” and “E300,” and is in the “Ascorbate” group within the food domain. In addition, also assume that the concept of side effects from food nutrients related to a consumer's health problem is “Ascorbic Acid” or “E300,” which appears on a food product's label. However, the consumer knows only that vitamin C has a side effect on him or her and does not know the meaning of “Ascorbic Acid” or “E300.” A smart system needs to recognize the risk, define an intolerance score, and warn the consumer before he or she consumes the selected food product. Thus, the system considers the entire set of related concepts, properties, individuals, or synonyms according to the consumer intolerance list.

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