Limits...
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

System working mechanism.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4496660&req=5

fig2: System working mechanism.

Mentions: User interfaces of the system are designed for ease of use, and they cater to user needs, working in online or offline modes, for use anywhere, anytime. First, the system gets the nutritional information of a selected product from the database according to its unique EAN number (through QR code or barcode number of the product; Step 1, Figure 2). Then, the system starts to search its ontology knowledge bases to retrieve nutritional information on the selected product and side effects of the nutrients according to the consumer's health or disease type (Step 2, Figure 2). In addition to retrieving the nutritional concepts about commercial products from the ontology, then the system compares that with the specific nutritional concepts that have side effects for consumer health (Step 3, Figure 2) through a concept-based matching task. In order to perform this comparison, the system needs to use semantic-based matching and reasoning through the concepts and relationships defined in the FOKB. The semantic matching of retrieved concepts and reasoning through FOKB are depicted as a system mechanism (Figure 2) and performed by a concept matching engine. The concept matching engine performs two sequence tasks: semantic enhancement of concepts as a first step and then concept matching tasks, which are further discussed below.


FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

System working mechanism.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: System working mechanism.
Mentions: User interfaces of the system are designed for ease of use, and they cater to user needs, working in online or offline modes, for use anywhere, anytime. First, the system gets the nutritional information of a selected product from the database according to its unique EAN number (through QR code or barcode number of the product; Step 1, Figure 2). Then, the system starts to search its ontology knowledge bases to retrieve nutritional information on the selected product and side effects of the nutrients according to the consumer's health or disease type (Step 2, Figure 2). In addition to retrieving the nutritional concepts about commercial products from the ontology, then the system compares that with the specific nutritional concepts that have side effects for consumer health (Step 3, Figure 2) through a concept-based matching task. In order to perform this comparison, the system needs to use semantic-based matching and reasoning through the concepts and relationships defined in the FOKB. The semantic matching of retrieved concepts and reasoning through FOKB are depicted as a system mechanism (Figure 2) and performed by a concept matching engine. The concept matching engine performs two sequence tasks: semantic enhancement of concepts as a first step and then concept matching tasks, which are further discussed below.

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