• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.151-166
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• 2003

The Topic Maps paradigm allows people and organizations to integrate and merge heterogeneous products classification systems such as UNSPSC and HS. Merging their product ontologies could combine information about classification scheme for products. We analyzed two product classification schemes for UML modeling and developed an integrated TM for watches . Examples in XTM syntax show how UNSPSC and HS can be integrated by merging their ontology.

• Journal of Digital Contents Society
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• v.18 no.2
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• pp.339-346
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• 2017

There is increasing interest in text analysis based on unstructured data such as articles and comments, questions and answers. This is because they can be used to identify, evaluate, predict, and recommend features from unstructured text data, which is the opinion of people. The same holds true for TEL, where the MOOC service has evolved to automate debating, questioning and answering services based on the teaching-learning support system in order to generate question topics and to automatically classify the topics relevant to new questions based on question and answer data accumulated in the system. Therefore, in this study, we propose topic modeling using LDA to automatically classify new query topics. The proposed method enables the generation of a dictionary of question topics and the automatic classification of topics relevant to new questions. Experimentation showed high automatic classification of over 0.7 in some queries. The more new queries were included in the various topics, the better the automatic classification results.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.503-512
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• 2024

This study examines the essays of academic background, written by students from a university, which is classified into prestigious universities in Korean society. By Latent Dirichlet Allocation, 172 essays were analyzed to explore the students' perspectives of the academic fractionalism. The analysis identified five topics such as, functional aspects (Topic 1), double-edged nature (Topic 2), power communities (Topic 3), symbols of victory (Topic 4), and dysfunctional aspects (Topic 5). The most frequently appearing keywords are 'individual,' 'status,' and 'means' in Topic 1, 'definition,' 'school,' and 'meaning' in Topic 2, 'people,' 'origin,' and 'power' in Topic 3, 'university,' 'ability,' and 'effort' in Topic 4, and 'academic achievement,' 'South Korea,' and 'origin' in Topic 5. By exploring the topics, we found that students regarded class reproduction by education as important social issues and they showed little interest in other factors influencing academic fractionalism, such as race or ethnicity. these findings suggest that professars, who teach the impact of education on academic fractionalism, deal with the influence of diverse factors on academic fractionalism.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.557-559
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• 2022

최근 딥러닝의 기술발전으로 자연어 처리 분야에서 Q&A, 문장추천, 개체명 인식 등 다양한 연구가 진행 되고 있다. 딥러닝 기반 자연어 처리에서 좋은 성능을 보이는 트랜스포머 기반 BERT 모델의 성능향상에 대한 다양한 연구도 함께 진행되고 있다. 본 논문에서는 토픽모델인 잠재 디리클레 할당을 이용한 토픽별 지식그래프 분류와 입력문장의 토픽을 추론하는 방법으로 K-BERT 모델을 학습한다. 분류된 토픽 지식그래프와 추론된 토픽을 이용해 K-BERT 모델에서 대용량 지식그래프 사용의 효율적 방법을 제안한다.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.774-779
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• 2008

In this work, we describe comments classification system using topic signature. Topic signature is widely used for selecting feature in document classification and summarization. Comments are short and have so many word spacing errors, special characters. We firstly convert comments into 7-gram. We consider the 7-gram as sentence. We convert the 7-gram into 3-gram. We consider the 3-gram as word. We select key feature using topic signature and classify new inputs by the Naive Bayesian method. From the result of experiments, we can see that the proposed method is outstanding over the previous methods.

• Journal of Korean Society of Archives and Records Management
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• v.16 no.1
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• pp.175-193
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• 2016

A topic map was designed as an efficient information retrieval method that is optimized for classification, organization, and navigation through the use of a semantic link network above information resources. With this, this study aims to design a topic map-based university archives retrieval system to provide the relevant information retrieval. For this study, electronic records that relate to the academic administration within two years of D university were collected, and topic map editing was carried out with Ontopia Omnigator. Topics were classified according to their functional analysis of academic administration. In the end, the number of topics was finalized as 626, with 6 types in general: academic work, staff, college register, student, university, etc. Association was separated into six types as well, which were formed with consideration to the relationships among topics. In addition, there are seven occurrence types: register class, register number, register date, receiver, title, creator, and identifier. It is expected that the associative nature of the designed topic map-based retrieval system in this study will make navigation of large records easy and allow incidental discovery of knowledge.

• Smart Media Journal
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• v.9 no.2
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• pp.69-77
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• 2020

Unlike the past, which was limited to terrestrial broadcasts, many dramas are currently being broadcast on cable channels and the Internet web. After watching the drama, viewers actively express their opinions through reviews and studies related to the analysis of these reviews are actively being conducted. Due to the nature of the drama, the genre is not clear, and due to the various age groups of viewers, reviews and ratings from other viewers help to decide which drama to watch. However, since it is difficult for viewers to check and analyze many reviews individually, a data analysis technique is required to automatically analyze them. Accordingly, this paper classifies the topics of reviews that have an important influence on drama selection and reclassifies them into semantic topics according to the similarity of words. In addition, we propose a model that classifies reviews into sentences according to semantic topics and sentiment analysis through sentiment words.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.148-151
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• 2006

웹 문서의 수가 기하급수적으로 늘어나는 현 시점에서 문서의 효율적인 관리을 위한 문서 클러스터링 방법은 현재 가장 요구되는 기술이다. 지금까지 문서 클러스터링의 방법 연구에서는 TF-Idf 측정값을 이용한 문서분류, Title 기반의 문서분류등과 같은 다양한 시도가 있었다. 이러한 문서 클러스터링 방법에서는 문서의 내용에 치중하거나 문서 분류를 위한 정확한 기준이 없어, 효율적인 문서의 클러스터링과 검색을 지원하지 못하였다. 그리하여, 본 연구에서는 새롭게 토픽 선정 알고리즘을 제안하고, 토픽 선정 알고리즘에 의해 결정된 토픽에 기반하여 문서 검색을 수행함으로써, 문서검색의 성능을 높일 수 있었다.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.61-77
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• 2017

Recently, the number of academic conference information on the Internet has rapidly increased, the automatic classification of academic conference information according to research subjects enables researchers to find the related academic conference efficiently. Information provided by most conference listing services is limited to title, date, location, and website URL. However, among these features, the only feature containing topical words is title, which causes information insufficiency problem. Therefore, we propose methods that aim to resolve information insufficiency problem by utilizing web contents. Specifically, the proposed methods the extract main contents from a HTML document collected by using a website URL. Based on the similarity between the title of a conference and its main contents, the topical keywords are selected to enforce the important keywords among the main contents. The experiment results conducted by using a real-world dataset showed that the use of additional information extracted from the conference websites is successful in improving the conference classification performances. We plan to further improve the accuracy of conference classification by considering the structure of websites.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.1-11
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• 2022

In this paper, numerical values are derived using topic modeling among data-based evaluation methodologies discussed by various research institutes. In addition, we will focus on the ICT field to see if there is a difference in policy perception between the national R&D project and standing committee. First, we create model for classifying ICT documents by learning R&D project data using HAN model. And we perform LDA topic modeling analysis on ICT documents classified by applying the model, compare the distribution with the topics derived from the R&D project data and proceedings of standing committees. Specifically, a total of 26 topics were derived. Also, R&D project data had professionally topics, and the standing committee-discuss relatively social and popular issues. As the difference in perception can be numerically confirmed, it can be used as a basic study on indicators that can be used for future policy or project evaluation.