• Journal of Korean Library and Information Science Society
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• v.47 no.4
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• pp.1-18
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• 2016

The purpose of this study is to explore application of topic modeling for topic analysis of book report. Topic modeling can be understood as one method of topic analysis. This analysis was conducted with texts in 23 book reports using LDA function of the "topicmodels" package provided by R. According to the result of topic modeling, 16 topics were extracted. The topic network was constructed by the relation between the topics and keywords, and the book report network was constructed by the relation between book report cases and topics. Next, Centrality analysis was conducted targeting the topic network and book report network. The result of this study is following these. First, 16 topics are shown as network which has one component. In other words, 16 topics are interrelated. Second, book report was divided into 2 groups, book reports with high centrality and book reports with low centrality. The former group has similarities with others, the latter group has differences with others in aspect of the topics of book reports. The result of topic modeling is useful to identify book reports' topics combining with network analysis.

• Proceedings of the Korean Society for Information Management Conference
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• 2005.08a
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• pp.403-408
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• 2005

현재 정보검색분야에서는 검색도구로써 시소러스가 갖는 장점에도 불구하고 기존에 개발된 시소러스의 유지관리와 활용이 극히 제한적으로 이루어지고 있기 때문이다. 왜냐하면 정보의 급격한 증가로 인하여 전통적인 시소러스의 구조와 유지관리, 활용기법으로는 현대 정보의 홍수 현상에 적극적으로 대처하는데 한계에 직면하였기 때문이다. 이러한 한계점을 극복하기 위해 토픽맵의 구축알고리즘이 절대적으로 필요하였다. 이에 따라 본 연구에서는 토픽맵의 기본요소인 토픽과 대상물, 연관관계, 토픽타입 등을 이용한 시소러스 구조화 알고리즘을 제안하였다. 특히 토픽맵의 기본 요소가운데 대상물(occurrence)은 시소러스의 검색효율가운데 정도율의 확보를 가능하게 하며, 시소러스의 구축에 필요한 지식베이스의 역할을 수행하는 주요한 기법임을 확인하였다.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.819-828
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• 2007

In this paper, we propose a topic maps matching and merging approach based on the syntactic or semantic characteristics and constraints of the topic maps. Previous schema matching approaches have been developed to enhance effectiveness and generality of matching techniques. However they are inefficient because the approaches should transform input ontologies into graphs and take into account all the nodes and edges of the graphs, which ended up requiring a great amount of processing time. Now, standard languages for developing ontologies are RDF/OWL and Topic Maps. In this paper, we propose an enhanced version of matching and merging technique based on topic partitioning, several matching operations and merging conflict detection.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.58-71
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• 2019

The purpose of this study is to explore the multi-dimensionality and the differences of the career success that is revealed by the employee's perception. In order to fulfill the research purpose, LDA topic modeling has applied to extract latent topics of career success from 126 Korean employees' open-end survey questionnaires. The extracted latent topics are social recognition, continuing service within an organization, expertise, financial rewards, and pursuing personal meaning. The occurrence probability of each topic was different by individual characteristics such as gender, education, position. Study findings showed there is multi-dimensionality in career success, and there are differences of topic occurrence probability by demographic characteristics. Additionally, this study showed how to apply the recently developed machine learning approach in order to reduce the researcher's bias by adapting the LDA topic modeling to the qualitative open-ended survey data.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.95-106
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• 2021

The purpose of this study is to derive major topics in content R&D and provide directions for academic development by exploring research trends over the past 20 years using topic modeling targeting 9,858 papers published in the Journal of the Korean Contents Association. To secure the reliability and validity of the extracted topics, not only the quantitative evaluation technique but also the qualitative technique were applied step-by-step and repeated until a corpus of the level agreed upon by the researchers was generated, and detailed analysis procedures were presented accordingly. As a result of the analysis, 8 core topics were extracted. This shows that the Korean Contents Association is publishing convergence and complex research papers in various fields without limiting to a specific academic field. Also, before 2012, the proportion of topics in the field of engineering and technology appeared relatively high, while after 2012, the proportion of topics in the field of social sciences appeared relatively high. Specifically, the topic of 'social welfare' showed a fourfold increase in the second half compared to the first half. Through topic-specific trend analysis, we focused on the turning point in time at which the inflection point of the trend line appeared, explored the external variables that affected the research trend of the topic, and identified the relationship between the topic and the external variable. It is hoped that the results of this study can provide implications for active discussions in domestic content-related R&D and industrial fields.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.397-412
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• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.73-80
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• 2022

In this study, prior studies of 280 small business owners in Korea over the past two decades were comprehensively analyzed through keyword network and LDA topic modeling analysis, and overall views and trends in academia were examined. As core keywords, "sales" and "protection," which conflict with each other but are essential for stable and sustainable growth were selected, and 7 topics (Topic 1: start-up, topic 2: digital, topic 3: tax system, topic 4: capability, topic 5: coexistence, topic 6: regulation, and topic 7: funding) were drawn up. Based on the results of the analysis, the need to improve digital maturity for the continued growth and development of small business owners was raised, and the response at the pan-ministerial level and the stability of the performance of functions that can survive even after the new administration to solve the economic damage problems facing small business owners were suggested. In addition, attention to the long-term, speed, detail, and direction of government support in a new way, and a flexible approach to the negative way in which pre-allowance and post-regulation is given were suggested.

• Proceedings of the Korean Society for Information Management Conference
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• 2010.08a
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• pp.41-44
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• 2010

본 논문은 토픽맵 기반 온톨로지 시스템의 통합효과 측정을 위한 연구에 앞서 통합의 가능성과 통합 성능을 측정하기 위한 프로토타입 시스템 구축에 관한 연구이다. 프로토타입 시스템 구축을 통해 자동 통합 툴의 성능을 측정하고자 한다. 이를 위해 통합 전의 단일 토픽맵에서의 검색 결과와 통합 토픽맵에서의 검색 결과를 비교하여 정답율과 재현율을 평가함으로써 통합 토픽맵이 정보의 손실 없이 단일 토픽맵들을 완전히 통합한 것인지 확인할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.240-243
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• 2011

웹 문서를 비롯한 여러 가지 문서의 양이 급증함에 따라, 문서로부터 주요정보를 얻거나 자동으로 요약하는 연구들이 진행되어왔다. 특히, 문서를 요약하는 연구들은 문서에 존재하는 문장을 추출하는 방법과 요약문을 새롭게 생성하는 방법, 이렇게 크게 두 가지 방법으로 진행되었다. 이 연구에서는, 잠재 토픽 모델을 통하여 얻어낸 각 문장의 토픽 순열을 이용하여 문서를 대표하는 문장, 즉 요약문으로서 적합한 문장들을 추출하는 새로운 기법을 소개한다. 특히, 잠재 토픽 모델이 일반적으로 가지고 있는 속성인 토픽 순열의 교환성(exchangeability)을 배제하고 토픽의 순열을 이용하여 요약문을 추출해내므로 이 기법을 통하여 문서 혹은 문장의 구조를 반영한 요약문을 만들 수 있다.

• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.149-152
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• 2014

본 연구에서는 저자명 모호성 해소를 위해 토픽모델링 기법을 사용하여 저자명을 식별 하였다. 기존의 토픽모델링은 용어 자질만을 고려하였지만 본 연구에서는 제 3의 메타데이터 자질을 활용하여 ACT(Author-Conference Topic Model) 모델과 DMR(Dirichlet-multinomial Regression) 토픽모델링을 대상으로 저자명 식별 성능을 평가, 비교하였다. 또한 수작업으로 저자 식별 작업을 한 데이터셋을 기반으로 저자 당 논문 수와 토픽 수에 차이를 두고 연구를 진행하였다. 그 결과 저자명 식별에 있어 ACT 모델보다 DMR 토픽모델링의 성능이 더 우수한 것을 알 수 있었다.