소셜빅데이터 분석을 위한 식품안전 온톨로지 구축 (Ontology of Food Safety for the Social Big Data Analysis)

Objectives: Anxiety of citizens on food safety is relatively high. Groundless anxiety on food could build mistaken dietary culture. Therefore, there needs to be risk communication based on verification for recognition of citizens on food safety. As existing questionnaire method or recognition survey via FGI have various limits, recognition survey through internet big data is certainly necessary. Object of this study is to develop food safety ontology which is surely necessary for big data analysis on food safety. Methods: In this study, all keywords of accidents relevant to food safety were derived from 62 precedent studies in 5 sectors such as food safety policies, food hygiene, recognition on food safety, activities relevant to food safety, the media as well as food gadgets(Food Journal) from 2011 to 2014. Those keywords were categorized and classified according to 5 phases which were modified and supplemented from its original development methodology named ‘Ontology Development 101’ in order to develop ontology. Results: Keywords related to food safety were classified into high classes including ‘Preventive factors’, ‘Generating factors’, ‘Consequence factors’, ‘Social relationship factors’ after classification for subjects with their common attributes. These high classes were categorized into middle class 1, middle class 2 and small class in order to develop final ontology. Conclusions: As food safety is directly connected with health of citizens, both big data research on it and development of ontology which is a research tool necessary for big data analysis is essential. Therefore, this research was significant thanks to development of ontology for food safety necessary for further various big data analysis on food safety at the first time.

Objectives: Anxiety of citizens on food safety is relatively high. Groundless anxiety on food could build mistaken dietary culture. Therefore, there needs to be risk communication based on verification for recognition of citizens on food safety. As existing questionnaire method or recognition survey via FGI have various limits, recognition survey through internet big data is certainly necessary. Object of this study is to develop food safety ontology which is surely necessary for big data analysis on food safety. Methods: In this study, all keywords of accidents relevant to food safety were derived from 62 precedent studies in 5 sectors such as food safety policies, food hygiene, recognition on food safety, activities relevant to food safety, the media as well as food gadgets(Food Journal) from 2011 to 2014. Those keywords were categorized and classified according to 5 phases which were modified and supplemented from its original development methodology named ‘Ontology Development 101’ in order to develop ontology. Results: Keywords related to food safety were classified into high classes including ‘Preventive factors’, ‘Generating factors’, ‘Consequence factors’, ‘Social relationship factors’ after classification for subjects with their common attributes. These high classes were categorized into middle class 1, middle class 2 and small class in order to develop final ontology. Conclusions: As food safety is directly connected with health of citizens, both big data research on it and development of ontology which is a research tool necessary for big data analysis is essential. Therefore, this research was significant thanks to development of ontology for food safety necessary for further various big data analysis on food safety at the first time.