• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2627-2647
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• 2016

With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.522-528
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• 2008

Topic map typically provide information to user through the selection of topics, that is using only topic, association, occurrence on the first topicmap which is made by domain expert without regard to individual interests or context, for the purpose of supplementation for the weakness which is providing personalized topic map information, personalization has been studied for supporting user preference through preseting of customize, filtering, scope, etc in topic map. Nevertheless, personalization in current topicmap is not enough to user so far. In this paper, we propose a design of PTRS(personalized topicmap ranking system) & algorithm, using both user profile(click through data) and basic element of topic map(topic, association) on knowledge layer in specific domain topicmap, therefore User has strong point that is improvement of personal facilities to user through representation of ranked topicmap information in consideration of user preference using PTRS.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.86-93
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• 2008

The construction of ontology defines as complicated semantic relations needs precise and expert skills. For the well defined ontology in real applications, plenty of information of instances for ontology classes is very critical. In this study, crawling algorithm which extracts the fittest topic from the Web overflowing over by a great number of documents has been focused and developed. Proposed crawling algorithm made a progress to gather documents at high speed by extracting topic-specific Link using URL patterns. And topic fitness of Link block text has been represented by fuzzy sets which will improve a precision of the focused crawler.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.629-641
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• 2016

Understanding of how teachers change instruction can help predict what kind of educational materials is supportive or appropriate. On the basis of this idea, we explored elementary teachers' PCK progression on specific topics of astronomy: planet size and distance in solar system. To identify the development of PCK over time, we utilized learning progression (LP) as a conceptual framework. The progression of teacher PCK can also be illustrated as the hypothetical pathway from novice to expert like LP. Eight 5th grade elementary teachers participated in this study. We observed participating teachers' astronomy classes with the same topic. In order to document topic-specific PCK of participating teachers, we developed an analytic protocol consisting of four categories: knowledge of curriculum, knowledge of teaching strategies, knowledge of assessment, and astronomical thinking practice. In addition, we monitored the changes in the four participating teachers' PCK for two years in order to validate the evidences of the PCK progression. Participating teachers in this study took some intervention by attending a four-week pre-meeting with the researchers to profile an adaptive instruction. Through this research, we profiled four and five different levels of PCK progressions in three knowledge components (curriculum, teaching strategies, student assessment) and one astronomical thinking practice (systems thinking), respectively. Participating teachers demonstrated various levels and pathways in each component of PCK. This study released the empirical evidences in fostering instructional scaffolding, which is appropriate to the level of PCK of science teachers on specific topic.

• Journal of Chest Surgery
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• v.49 no.6
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• pp.413-420
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• 2016

Doctors are often faced with difficult decisions and uncertainty when patients need a certain treatment. They routinely rely on the scientific literature, in addition to their knowledge, experience, and patient preferences. Clinical practice guidelines are created with the intention of facilitating decision-making. They may offer concise instructions for the diagnosis, management (medical or surgical treatments), and prevention of specific diseases or conditions. All information included in the final version are the result of a systematic review of scientific articles and an assessment of the benefits and costs of alternative care options. The final document attempts to meet the needs of most patients in most circumstances and clinicians, aware of these recommendations, should always make individualized treatment decisions. In this review, we attempted to define the intent and applicability of clinical practice guidelines, expert consensus documents, and registry studies, focusing on the management of patients with thoracic aortic disease.

• The KIPS Transactions:PartA
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• v.11A no.3
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• pp.217-226
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• 2004

This paper describes an experiment of concrete and specific group work learning system based on the traditional Jigsaw group work learning model. Jigsaw model has two groups of students such as random group and expert group so that a course can make progress on explaining and lecturing all members of class after each student can be a member of expert group of course topic. We design and implement Web-based training system to support collaboration and Interaction among students of a course based on Jigsaw model The Web- based learning system makes each group going up to the expert level of a course subject by supporting various study menu and provides equal opportunity of improving social abilities such as leadership, communication skill, trust, and trouble-settling by taking part in collaboration activities.

• The Korean Journal of Petroleum Geology
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• v.6 no.1_2 s.7
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• pp.20-24
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• 1998

Expert system is one type of artificial intelligence softwares that incorporate problem-solving knowledges and experiences of human experts by use of symbolic reasoning and rules about a specific topic. In this study, an expert system, PLAYMAKER2, is used to interpret sedimentary facies and depositional settings of the sedimentary sequence. The original version of the expert system, PLAYMAKER, was developed in University of South Carolina in 1990, and modified into the present PLAYMAKER2 with some changes in the knowledge-base of the previous system. The late Cenozoic sedimentary sequence with maximum 10,000 m in thickness, which is located in the Korean Oil Exploration Block VI-1 at the southwestern margin of the Ulleung Basin, is analysed by the expert system, PLAYMAKER2. The Cenozoic sedimentary sequence is divided into two units-lower Miocene and upper Pliocene-Pleistocene sediments. The depositional settings and sedimentary facies of the Miocene sediments interpreted by PLAYMAKER2 in terms of belief values are: for depositional settings, slope; $57.4\%$, shelf; $21.4\%$, basin; $10.1\%$, and for sedimentary facies, submarine fan; $35.7\%$, continental slope; $26.3\%$, delta; $16.1\%$, deep basinplain; $6.1\%$ continental shelf; $3.2\%$, shelf margin; $1.4\%$. The depositional settings and sedimentary facies of the Pliocene-Pleistocene sediments in terms of belief values we: for depositional settings, slope; $59.0\%$, shelf; $22.8\%$, basin; $7.0\%$, and for sedimentary facies, delta; $24.1\%$, continental slope; $22.2\%$, submarine fan; $17.3\%$, continental shelf; $7.0\%$, deep basinplain; $4.8\%$, shelf margin; $2.6\%$. The comparison of the depositional settings and sedimentary facies consulted by PLAYMAKER2 with those of the classical interpretation from previous studies shows resonable similarity for the both sedimentary units-the lower Miocene sediments and the upper Pliocene-Pleistocene sediments. It demonstrates that PLAYMAKER2 is an efficient tool to interpret the depositional setting and sedimentary facies for sediments. However, to be a more reliable system, many sedimentologists should work to refine and add geological rules in the knowledge-base of the expert system, PLAYMAKER2.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.380-386
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• 2020

The mobile application market has grown over the past decade since the advent of smartphones, making it the largest market for electronic device software. As competition intensifies in the mobile application market, the impact of application evaluations on the consumption and usage patterns of users has also significantly increased. Therefore, research has been conducted on measures to evaluate mobile applications, but most of the research has relied on qualitative methods such as expert-centered interviews or surveys. In addition, evaluation measures are being constructed from the service provider's perspective, not from the service user's perspective. However, the possibility of application-specific analyses that minimize the subjectivity of researchers is growing, as large amounts of user review data enable quantitative analysis of actual users' assessment of applications. Therefore, this study presents a methodology that can complement current problems with existing quality assessments for mobile applications by utilizing user review data. To this end, the Topic Modeling technique LDA (Latent Dirichlet allocation) is applied in order to elucidate ways to improve existing evaluation measures from a user's perspective. The study is expected to reduce bias in service assessment due to the subjectivity of service providers and researchers as well as provide a measure of assessment by area of mobile applications from a consumer perspective.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.