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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

getIntoleranceScore (CList, PList).
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Related In: Results  -  Collection


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alg2: getIntoleranceScore (CList, PList).

Mentions: As shown in Algorithm 2,  getIntoleranceScore(CList,  PList) uses two lists, C and P, to find the total similarity score in the second step of the CME. The first parameter is a list of concepts of the ingredient side effects concepts C = {C1, C2,…, Cm}, while the second is a list of concepts of selected product's ingredients P = {P1, P2,…, Pn}. During this step, the hasGroup( ), hasAdditiveType( ), hasSynonym( ), or hasIs_a( ) properties or class IRI concepts of the semantically enhanced consumer intolerance list (C) are analyzed by recovering their semantics, that is, meaning, similarities, differences, and relations, while carrying out Algorithm 1. Similarly, the semantic distances between concepts, which offer similarity information between concepts, can be provided by the ontology developer during the development phase:(1)dweightA=1#ofSubconceptsofA.If semantic distances are not scored by the developer, all direct subconcepts of a parent concept will have the same distance weight (Bener et al. [12–14]) according to (1).


FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

Çelik D - ScientificWorldJournal (2015)

getIntoleranceScore (CList, PList).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

alg2: getIntoleranceScore (CList, PList).
Mentions: As shown in Algorithm 2,  getIntoleranceScore(CList,  PList) uses two lists, C and P, to find the total similarity score in the second step of the CME. The first parameter is a list of concepts of the ingredient side effects concepts C = {C1, C2,…, Cm}, while the second is a list of concepts of selected product's ingredients P = {P1, P2,…, Pn}. During this step, the hasGroup( ), hasAdditiveType( ), hasSynonym( ), or hasIs_a( ) properties or class IRI concepts of the semantically enhanced consumer intolerance list (C) are analyzed by recovering their semantics, that is, meaning, similarities, differences, and relations, while carrying out Algorithm 1. Similarly, the semantic distances between concepts, which offer similarity information between concepts, can be provided by the ontology developer during the development phase:(1)dweightA=1#ofSubconceptsofA.If semantic distances are not scored by the developer, all direct subconcepts of a parent concept will have the same distance weight (Bener et al. [12–14]) according to (1).

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