• Proceedings of the Fisheries Business Administration Society of Korea Conference
• /
• 2007.06a
• /
• pp.93-112
• /
• 2007

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1273-1284
• /
• 2016

In this paper, we analyzed the patents on machine learning using keyword network analysis and clique analysis. To construct a keyword network, important keywords were extracted based on the TF-IDF weight and their association, and network structure analysis and clique analysis was performed. Density and clustering coefficient of the patent keyword network are low, which shows that patent keywords on machine learning are weakly connected with each other. It is because the important patents on machine learning are mainly registered in the application system of machine learning rather thant machine learning techniques. Also, our results of clique analysis showed that the keywords found by cliques in 2005 patents are the subjects such as newsmaker verification, product forecasting, virus detection, biomarkers, and workflow management, while those in 2015 patents contain the subjects such as digital imaging, payment card, calling system, mammogram system, price prediction, etc. The clique analysis can be used not only for identifying specialized subjects, but also for search keywords in patent search systems.

• Journal of Intelligence and Information Systems
• /
• v.11 no.2
• /
• pp.1-15
• /
• 2005

The explosive growth of the Internet leads to various on-line shopping malls and active E-Commerce. however, as the internet has experienced continuous growth, users have to face a variety and a huge amount of items, and often waste a lot of time on purchasing items that are relevant to their interests. To overcome this problem the comparison shopping systems, which can help to compare items' information with those other shopping malls, have been issued as a solution. However, when users do not have much knowledge what they want to find, a keyword-based searching in the existing comparison shopping systems lead users to waste time for searching information. Thereby, the performance is fell down. To solve this problem in this research, we suggest the Comparison Shopping System using Image Retrieval based on Semantic Web. The proposed system can assist users who don't know items' information that they want to find and serve users for quickly comparing information among the items. In the proposed system we use semantic web technology. We insert the Semantic Annotation based on Ontology into items' image of each shopping mall. Consequently, we employ those images for searching the items instead of using a complex keyword. In order to evaluate performance of the proposed system we compare our experimental results with those of Keyword-based Comparison Shopping System and simple Semantic Web-based Comparison Shopping System. Our result shows that the proposed system has improved performance in comparison with the other systems.

• Journal of Korea Multimedia Society
• /
• v.19 no.11
• /
• pp.1878-1890
• /
• 2016

Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

• Journal of Korean Library and Information Science Society
• /
• v.45 no.1
• /
• pp.149-171
• /
• 2014

This study makes some suggestions to cluster works from the search result of library databases. Specifically, the author suggests modifying the current practices and rules of KORMARC 24X field based on intent of RDA, which is the new cataloging standard. After identifying some problems in entering bibliographic data into 24X field, the author proposes solutions to such problems. The solutions include improvements in (1) Goanje description (관제기술), (2) use of uniform title, and (3) processing of marks, etc. The study demonstrates that users can conduct a fronted keyword searching effectively and identify the relevant bibliographic records easily from the clustered search results.

• The Journal of the Korea Contents Association
• /
• v.18 no.7
• /
• pp.449-458
• /
• 2018

As the usage of social network services increases, event information occurring offline is spreading more rapidly. Therefore, studies have been conducted to detect events by analyzing social data. In this paper, we propose a graph based event detection scheme considering user interest in social networks. The proposed scheme constructs a keyword graph by analyzing tweets posted by users. We calculates the interest measure from users' social activities and uses it to identify events by considering changes in interest. Therefore, it is possible to eliminate events that are repeatedly posted without meaning and improve the reliability of the results. We conduct various performance evaluations to demonstrate the superiority of the proposed event detection scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.462-463
• /
• 2018

Traditional document analysis systems used word-based analysis using a morphological analyzer or TF-IDF technique. These systems have the advantage of being able to derive key keywords by calculating the weights of the keywords. On the other hand, it is not appropriate to analyze the contents of documents due to the structural limitations. To solve this problem, the proposed algorithm calculates the weights of the documents in the document and divides the paragraphs into areas. And we calculate the importance of the divided regions and let the user know the area with the most important paragraphs in the document. So, it is expected that the user will be provided with a service suitable for analyzing documents rather than using existing document analysis systems.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.2
• /
• pp.23-31
• /
• 2003

In general, people use a key word or a phrase as the name of field or subject word in document. This paper has focused on keyword extraction. First of all, we investigate that an author suggests keywords that are not occurred as contents words in literature, and present generation rules to combine compound keywords based on concept of lexical information. Moreover, we present a new importance measurement to avoid useless keywords that are not related to documents' contents. To verify the validity of extraction result, we collect titles and abstracts from research papers about natural language and/or voice processing studies, and obtain the 96% precision in a top rank of extraction result.

• Korean Journal of Oriental Medicine
• /
• v.17 no.2
• /
• pp.57-62
• /
• 2011

We in this paper propose a semantic search system using the basic ontology in Korean medicine field. The basic ontology provides a formalization of medicinal materials, formulas, and diseases of Korean medicine. Recently, many studies for the semantic search system have been proposed. However, they do not support the semantic search and reasoning in the domain of Korean medicine because they do not have the Korean medicine ontology. Our system provides the semantic search features of semantic keyword recommendation, associated information browsing, and ontology reasoning based on the basic ontology. In addition, they also have the features of ontology search of a form of table and graph, synonym search, and external Open API supports. The general search engines usually provide search results for the simple keyword, while our system can also provide the associated information with respect to search results by using ontology so that can recommend more exact results to users.

• KIISE Transactions on Computing Practices
• /
• v.22 no.5
• /
• pp.235-239
• /
• 2016

'Commit-bug link', the link between commit history and bug reports, is used for software maintenance and defect prediction in bug tracking systems. Previous studies have shown that the links are automatically detected based on text similarity, time interval, and keyword. Existing approaches depend on the quality of commit history and could thus miss several links. In this paper, we proposed a technique to link commit and bug report using not only messages of commit history, but also the similarity of files in the commit history coupled with bug reports. The experimental results demonstrated the applicability of the suggested approach.