• Korean journal of communication and information
• /
• v.61
• /
• pp.159-179
• /
• 2013

This study aims to consider the criteria and signification of 'aging' constructed in media in Korean society that has entered aging society. For the purpose, this study analyzed KBS the representative TV health programs. According to the result, designs the measurable indexes of aging to rank the casts. And it emphasizes to the casts that cannot reach a certain level the support from medical experts or advanced medical technology. With such characteristics of individual text, this paper found the ideological codes of the health programs. They contrast the elderly who have achieved successful aging from those that have not. They define the aged who have not practiced self-management or medical control to prevent aging properly as failure and also make fun of them. They draw aging that was not regarded as some kind of disease in the past into the area of medicine. Besides, the medicalization of aging regarded as an object for treatment may come to strengthen the control of medical experts and also individualize social issues.

• KIISE Transactions on Computing Practices
• /
• v.23 no.1
• /
• pp.28-36
• /
• 2017

After the genome projects of the 90s, a vast number of gene studies have been stored in online databases. By using these databases, several biological relationships can be inferred. In this study, we proposed a method to infer disease-gene relationships using title and body in biomedical text. The title was used to extract hub genes from data in the literature; whereas, the body of the literature was used to extract sub genes that are related to hub genes. Through these steps, we were able to construct a local gene-network for each report in the literature. By integrating the local gene-networks, we then constructed a global gene-network. Subsequent analyses of the global gene-network allowed inference of disease-related genes with high rank. We validated the proposed method by comparing with previous methods. The results indicated that the proposed method is a meaningful approach to infer disease-related genes.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.347-358
• /
• 2003

VSField-associated term is a single or compound word whose terms occur in any document, and which makes it possible to recognize a field of text by using common knowledge of human. For example, human recognizes the field of document such as or , a field name of text, when she encounters a word 'Pitcher' or 'election', respectively We Proposes an efficient construction method of field-associated terms (FTs) for specializing field to decide a field of text. We could fix document classification scheme from well-classified document database or corpus. Considering focus field we discuss levels and stability ranks of field-associated terms. To construct a balanced FT collection, we construct a single FTs. From the collections we could automatically construct FT's levels, and stability ranks. We propose a new extraction algorithms of FT's for document classification by using FT's concentration rate, its occurrence frequencies.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2000.04a
• /
• pp.380-384
• /
• 2000

Effectiveness of information conveyance in reading is affected by several factors such as line length, letter size, line spacing arrangement as well as typeface itself. This study examined relative importance of these factors by asking people to read the texts that was constituted with non-square form letter and rank the preference of texts through conjoint analysis. In the case of reading time, justification was the most important factor, followed by leading, line spacing, letter width, line length, font size, font type in their order of importance. And in the case of preference decision, letter width was the most important factor, followed by font size, justification, line spacing, leading, line length, font type. The result will be useful in understanding how to consider human preference in the hangul typography.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.1
• /
• pp.124-129
• /
• 2006

This parer presents a method of ranking translation word selection for Korean verbs based on lexical knowledge contained in a bilingual Korean-English dictionary and WordNet that are easily obtainable knowledge resources. We focus on deciding which translation of the target word is the most appropriate using the measure of semantic relatedness through the 45 extended relations between possible translations of target word and some indicative clue words that play a role of predicate-arguments in source language text. In order to reduce the weight of application of possibly unwanted senses, we rank the possible word senses for each translation word by measuring semantic similarity between the translation word and its near synonyms. We report an average accuracy of $51\%$ with ten Korean ambiguous verbs. The evaluation suggests that our approach outperforms the default baseline performance and previous works.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.852-854
• /
• 2019

본 논문은 정확한 연관검색어를 보여주지 못하는 기존의 검색에서 벗어나기 위해 이미지와 PDF에서 텍스트를 추출하고 키워드 요약하는 방법을 사용하였다. 텍스트를 키워드로 요약하는 알고리즘으로는 TextRank, LSA, MMR을 사용하였고, 세 가지 방법으로 키워드를 요약하고 키워드 요약 결과와 Query의 코사인 유사도를 이용하여 추출한 문서와 Query와의 연관성을 확인하여 세 가지 알고리즘을 비교하였다.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.75-88
• /
• 2023

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

• The Journal of Society for e-Business Studies
• /
• v.20 no.4
• /
• pp.61-76
• /
• 2015

In general, we have utilized the hierarchical concept tree as a crucial data structure for indexing huge amount of textual data. This paper proposes a generality rank-based method that can automatically develop hierarchical concept structures with the Wikipedia data. The goal of the method is to regard each of Wikipedia articles as a concept and to generate hierarchical relationships among concepts. In order to estimate the generality of concepts, we have devised a special ranking function that mainly uses the number of hyperlinks among Wikipedia articles. The ranking function is effectively used for computing the probabilistic subsumption among concepts, which allows to generate relatively more stable hierarchical structures. Eventually, a set of concept pairs with hierarchical relationship is visualized as a DAG (directed acyclic graph). Through the empirical analysis using the concept hierarchy of Open Directory Project, we proved that the proposed method outperforms a representative baseline method and it can automatically extract concept hierarchies with high accuracy.

• Korean Journal of Cognitive Science
• /
• v.19 no.4
• /
• pp.459-476
• /
• 2008

The user needs to find the answer to your question is growing fast at the service using collective intelligent knowledge. In the previous researches, it was proven that the non-text information like view counting, referrer number, and number of answer is good in evaluating answers. There were also many works about evaluating answers using the various kinds of word dictionaries. In this work, we propose new method to evaluate answers to question effectively using user reputation that estimated by the social activity. We use a modified PageRank algorithm for estimating user reputation. We also use the similarity between question and answer. From the result of experiment in the Naver GisikiN corpus, we can see that the proposed method gives meaningful performance to complement the answer selection rate.

• Journal of The Korean Association of Information Education
• /
• v.13 no.1
• /
• pp.69-76
• /
• 2009

This paper proposes a teaching plan of programming language class for university of education students amusingly with LEGO Mindstorms NXT robot. The goal of class is not fragmentary knowledge acquirement but problem-solving of maze. This robot communicates with GUI named NXT-G installed in computer via USB. GUI is not text-based but icon-based programming tool. This paper designs a semester with 3 steps such as beginner, intermediate, high-rank. Beginner step is consists of learning of basic functions such as GUI usage and several sensors of robot. Intermediate step is consists of solving of maze problem with low complexity. High-rank step is consists of solving maze problem with medium and high complexity. All maze problem-solving have 3 process with algorithm, flowchart, and programming with stack.