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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

The customer member chooses various products and gets the results on the android mobile application of the system though FOKB.
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fig8: The customer member chooses various products and gets the results on the android mobile application of the system though FOKB.

Mentions: In our scenario, Ms. Celik, who has gluten allergy, logs into the application system while she is shopping in a supermarket. She has a smart phone that is able to connect to the FoodWiki mobile application. She connects to the system via her username and password (Figure 8(a)). If the consumer has not registered before, then she can create a new user account for herself (Figures 8(b) and 8(c)). She can see the profile details as is shown in Figure 8(d). She wants to check a product on the market shelves in order to buy suitable ingredients for her health condition, that is, gluten allergy (Figure 8(e)). She then chooses a packaged food product from a shelf (e.g., the product is ETİ chocolate in Figure 8(f)). She wants to be sure that the chosen product will not cause any side effects for her condition since she has intolerances for the gluten risk type of wheat, glutamic acid, and wheat flour, and so forth. She scans the QR code/barcode of the product on her mobile phone (or alternatively through a market kiosk) and then the system retrieves all the nutritional information related to that product from the database of the ministry through its search product web services (Figure 8(f)). Then, the system searches the nutritional information to get the indicated concepts in the FOKB. In addition to retrieving the nutritional concepts of commercial products, the system is able to compare them with specific nutritional concepts affecting her health (i.e., see the warning messages and intolerance rate on screens; Figure 8(g)).


FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

The customer member chooses various products and gets the results on the android mobile application of the system though FOKB.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: The customer member chooses various products and gets the results on the android mobile application of the system though FOKB.
Mentions: In our scenario, Ms. Celik, who has gluten allergy, logs into the application system while she is shopping in a supermarket. She has a smart phone that is able to connect to the FoodWiki mobile application. She connects to the system via her username and password (Figure 8(a)). If the consumer has not registered before, then she can create a new user account for herself (Figures 8(b) and 8(c)). She can see the profile details as is shown in Figure 8(d). She wants to check a product on the market shelves in order to buy suitable ingredients for her health condition, that is, gluten allergy (Figure 8(e)). She then chooses a packaged food product from a shelf (e.g., the product is ETİ chocolate in Figure 8(f)). She wants to be sure that the chosen product will not cause any side effects for her condition since she has intolerances for the gluten risk type of wheat, glutamic acid, and wheat flour, and so forth. She scans the QR code/barcode of the product on her mobile phone (or alternatively through a market kiosk) and then the system retrieves all the nutritional information related to that product from the database of the ministry through its search product web services (Figure 8(f)). Then, the system searches the nutritional information to get the indicated concepts in the FOKB. In addition to retrieving the nutritional concepts of commercial products, the system is able to compare them with specific nutritional concepts affecting her health (i.e., see the warning messages and intolerance rate on screens; Figure 8(g)).

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