• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1049-1052
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• 2002

기존의 키워드 추출 방법은 출현회수(frequency)에 기반한 가중치(weight) 부여 방식이 많이 쓰였다. 본 논문에서는 HTML 문서와 같이 스타일이 적용된 문서의 경우 출현회수와 함께 단어에 적용된 스타일을 고려하여 가중치를 부여해 키워드를 추출하는 방법을 제안한다. 가중치를 부여할 스타일 항목과 항목별 가중치 부여방법을 정의하고 이를 단어별로 합산하고 정규화(normalization)하는 방법을 정의하여 스타일에 기반 해 키워드를 추출하였다. 내용이 특정된 도메인으로부터 순위(ranking)가 매겨진 도메인 키워드 리스트를 뽑아서 이를 기준으로 삼아 기존의 출현회수 기반의 키워드 추출 방식과 양적, 질적인 비교를 수행하여 우월함을 보였다.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.271-277
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• 2013

Typical semantic search query for Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. Existing keyword-based retrieval system is Preference for the semantic interpretation of a user's query is not the meaning of the user keywords of interconnect, you can not search. In this paper, we propose a method that can provide accurate results to meet the user's search intent to user preference based evaluation by ranking search. The proposed scheme is Integrated ontology-based knowledge base built on the formal structure of the semantic interpretation process based on ontology knowledge base system.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.157-163
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• 2018

People who use SNS publish their diverse ideas on SNS every day. The data posted on the SNS contains many people's thoughts and opinions. In particular, popular keywords served on Twitter compile the number of frequently appearing words in user posts and rank them. However, this method is sensitive to unnecessary data simply by listing duplicate words. The proposed method determines the ranking based on the topic of the word using the relationship diagram between words, so that the influence of unnecessary data is less and the main word can be stably extracted. For the performance comparison in terms of the descending keyword rank and the ratios of meaningless keywords among high rank 20 keywords, we make a comparison between the proposed scheme which is based on morphological analysis and PageRank, and the existing scheme which is based on the number of appearances. As a result, the proposed scheme and the existing scheme have included 55% and 70% of meaningless keywords among high rank 20 keywords, respectively, where the proposed scheme is improved about 15% compared with the existing scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.721-732
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• 2021

This study analyzed keywords for agricultural R&D using the textmining method to examine the trend of agricultural R&D. Data used for the analysis included R&D project information provided by NTIS, and the research and development step by year from 2003 to 2018 were classified and applied. The TF-IDF approach was used as the analysis method, and ranking was derived based on score. Furthermore, we analyzed by grouping for similar keywords. The main analysis results are as follows. First, agricultural R&D trends are changing according to the introduction of new technologies and changes in the external environment. Second, keyword changes appeared with a time lag in the R&D step. The main keywords are changing in the order of basic research - applied research - development research. Third, the main keyword of agricultural R&D was 'rice.' However, the direction and purpose of the research were changing according to changes in the domestic and foreign agricultural environments.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.315-322
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• 2008

It is important to retrieve effectively the visual media such as pictures and video in the internet, especially to the application areas such as electronic art museum, e-commerce, and internet shopping malls. It is also needed in these areas to have content-based or even semantic-based multimedia retrieval instead of simple keyword-based retrieval. In our earlier research, we proposed a semantic-based visual media retrieval framework for the effective retrieval of the visual media from the internet. It uses visual media metadata and ontology based on the web service to achieve the semantic-based retrieval. In this research, there are more than one visual media service providers and one central service broker. As a preliminary step to the visual media data retrieval, a method is proposed to retrieve the service providers effectively. The method uses the structure of the ontology tree to obtain the providers and their rankings. It also uses the size of sub nodes and child nodes in the tree. It measures the rankings of providers more effectively than previous method. The experimental results show the accuracy of the method while keeping compatible speed against the existing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.529-532
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• 2016

본 논문은 기존의 TextRank 알고리즘에 상호정보량 척도를 결합하여 군집 기반에서 키워드 추출하는 ClusterTextRank 기법을 제안한다. 제안 기법은 k-means 군집화 알고리즘을 이용하여 문서들을 여러 군집으로 나누고, 각 군집에 포함된 단어들을 최소신장트리 그래프로 표현한 후 이에 근거한 군집 정보량을 고려하여 키워드를 추출한다. 제안 기법의 성능을 평가하기 위해 여행 관련 블로그 데이터를 이용하였으며, 제안 기법이 기존 TextRank 알고리즘보다 키워드 추출의 정확도가 약 13% 가량 개선됨을 보인다.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.147-156
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• 2006

Event logging plays increasingly an important role in system and network management, and syslog is a de-facto standard for logging system events. However, due to the semi-structured features of Common Log Format data most studies on log analysis focus on the frequent patterns. The extensible Markup Language can provide a nice representation scheme for structure and search of formatted data found in syslog messages. However, previous XML-formatted schemes and applications for system logging are not suitable for semantic approach such as ranking based search or similarity measurement for log data. In this paper, based on ranked keyword search techniques over XML document, we propose an XML tree structure through a new data modeling approach for syslog data. Finally, we show suitability of proposed structure for semantic retrieval.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.1-9
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• 2010

In the new generation of Web, commonly called "Web 2.0", blogging has facilitated the publishing information or his/her opinion on the web. Various blog retrieval algorithms have been proposed to search for blogs more effectively. However, actually keyword-based searching or link-analysis blog ranking system cannot satisfy the user's requirement. In this paper, we suggest a topic-based weighting blog retrieval system in which the links between blog writings and searching words are considered to improve the search results. Our system extracts topics from each blog and weights them much higher than other guide words. In the comparison with other systems, we see that the proposed topic-base system has better recall rate of search results.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.369-380
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• 2004

In this paper, we propose a web page weighting scheme based on WordNet-based collaborative evaluation and hyperlink to improve the precision of web search engine. Generally search engines use keyword matching to decide web page ranking. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy. In this paper, we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to lenses, so that more accurate search is possible. Weighting of each web page by using collaborative evaluation and hyperlink is reflected in ranking. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.