• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of Preventive Medicine and Public Health
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• v.27 no.2 s.46
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• pp.200-216
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• 1994

In present study, the authors investigated the possible effect of coffee consumption on serum cholesterol level in 1017 men between the ages of 40 and 59 years, who were randomly selected from the members of Seoul Cohort Study. Serum total cholesterol data was collected with other serologic indices (e.g. systolic blood pressure, diastolic blood pressure, hight, weight, etc.) through the program of biennial health check-up offered by Korean Medical Insurance Corporation (KMIC). The amount of coffee consumption was assessed by a self-administered questionnaire through mailing. Other confounding factors, such as age, body mass index, cigarette smoking, alcohol consumption, physical activity, and other dietary intake pattern were also determined by the questionnaire. The differences in means of serum total cholesterol in compared to non consumers were $-0.4{\pm}3.56mg/dl$ for those drinking less than 1 cup a day, $-0.6{\pm}3.60mg/dl$ for those drinking 1 cup a day, and $7.1{\pm}3.41mg/dl$ for those drinking more than 2 cups a day. Since smoking interacted the relationship between coffee consumption and serum total choleaterol, we re-analyzed those relationship in smokers and non-smokers separately Other atherogenic behaviors were well correlated with total cholesterol, so we adjusted the mean values of serum total cholesterol through multivariate model selection with age(r=0.12), total cigarette index (cigarette-years; r=0.10), Quetelet's index ($Kg/m^2$, r=0.16), daily calory expenditure (kcal/day, r=0.06), weekly meat and poultry consumption(g/week, r=0.05), weekly fish consumption (g/week, r=0.08), other caffeinated beverage intake (cups/week), and the amount of sugar and prim added to the coffee. Among those variables only age, Quetelet's index, fish consumption, and total cigarette index (in smokers) were remained in the models. After adjustment, the corresponing differences of total cholesterol in smokers were changed to $0.4{\pm}5.24mg/dl,\;-0.5{\pm}4.97mg/dl,\;and\;8.9{\pm}4.78mg/dl$, which were significantly different among themselves (P=0.011). In non-smokers, however, the differences were not statistically significant (P=0.76). Adjusted mean values of systolic blood pressure and diastolic blood pressure were also determined to evaluate the direct effect of coffee to cardiovascular system, but their means were not significantly different by coffee consumption(p=0.18 for SBP, p=0.48 for DBP). Asuming instant coffee in the most popular type of coffee in Korea, the association observed in our study between coffee and serum total cholesterol, especially in smokers, is very interesting finding for the connection between coffee and serum total cholesterol, because only 'boiled coffee' tend to show significant lipid raising effect rather than to other types of coffee, like filtered or espresso, in most of the western countries. We concluded that people who drink coffee more than 2 cups a day have significantly higher serum total cholesterol level than those who never drink coffee, especially in smokers.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.