• The Journal of the Korea Contents Association
• /
• v.7 no.5
• /
• pp.16-26
• /
• 2007

Because it is difficult on existing document retrieval systems to visualize the search result, search results show document titles and short summaries of the parts that include the search keywords. If the result list is long, it is difficult to examine all the documents at once and to find a relation among them. This study uses clustering to classify similar documents into groups to make it easy to grasp the relations among the searched documents. Also, this study proposes a two-level visualization algorithm such that, first, the center of clusters is projected to low-dimensional space by using multi-dimensional scaling to help searchers grasp the relation among clusters at a glance, and second, individual documents are drawn in low-dimensional space based on the center of clusters using the orbital model as a basis to easily confirm similarities among individual documents. This study is tested on the benchmark data and the real data, and it shows that it is possible to visualize search results in real time.

• Journal of Internet Computing and Services
• /
• v.2 no.2
• /
• pp.1-9
• /
• 2001

To access massive and various HTML documents that are in the web using wireless Internet equipments, another WML document that is equal to the HTML document must be written. In the case Web documents written by HTML are massive, the construction of a WML site with the same information needs much cost of space and time. This paper designs and implements the HTML-XML converter that alleviates such a problem. The HTML-XML converter translates the Web document written by HTML to the WML document for portable wireless equipments. The HTML-XML converter has advantages that it reconstructs WML document dynamically according to portable wireless equipments and processes various image formats such as GIF, JPG, BMP, and so on. The HTML-WML converter can be used as not only a utility of the WML editor but also a real time converter on wireless internet.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.21-41
• /
• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• Journal of KIISE:Databases
• /
• v.37 no.1
• /
• pp.50-55
• /
• 2010

Duplicate documents in blog service system are one of causes that deteriorate both of the quality and the performance of blog searches. Unlike the WWW environment, the creation of documents is reported every time in blog service system, which makes it possible to identify the original document from its duplicate documents. Based on this observation, this paper proposes a novel method for detecting duplication documents in blog service system. This method determines whether a document is original or not at the time it is stored in the blog service system. As a result, it solves the problem of duplicate documents retrieved in the search result by keeping those documents from being stored in the index for the blog search engine. This paper also proposes three indexing methods that preserve an accuracy of previous work, Min-hashing. We show most effective indexing method via extensive experiments using real-life blog data.

• Proceedings of the Korea Contents Association Conference
• /
• 2007.11a
• /
• pp.346-349
• /
• 2007

In this paper, the integrated search system made for the web news and review retrieval service is introduced. We made XSLTRobot that extract title, date, author and content from html document like news or reviews for search service. XSLTRobot used the XSLT technology in order to extract desired part of html page. The Intergrated Information Retrieval System(IIRS) is suitable for various search data format. And we introduce Dynamic Index Storage which is module of IIRS. Dynamic Index Storage is used to environment which needs fast index update like news. And it's design focused on retrieval performance because there was not many document that it has to update on a real time.

• The KIPS Transactions:PartB
• /
• v.16B no.2
• /
• pp.181-194
• /
• 2009

With the increased interest on the document copyright, many of researches related to the document plagiarism have been done up to now. The plagiarism problem of newspaper articles has attracted much interest because the plagiarism cases of the articles having much commercial values in market are currently happened very often. Many researches related to the document plagiarism have been so hard to be applied to the newspaper articles because they have strong real-time characteristics. So to detect the plagiarism of the articles, many human detectors have to read every single thousands of articles published by hundreds of newspaper companies manually. In this paper, we firstly sorted out the articles with high possibility of being copied by utilizing OpenAPI modules supported by web search companies such as Naver and Daum. Then, we measured the document similarity between selected articles and the original article and made the system decide whether the article was plagiarized or not. In experiment, we used YonHap News articles as the original articles and we also made the system select the suspicious articles from all searched articles by Naver and Daum news search services.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.10
• /
• pp.103-109
• /
• 2018

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

• The KIPS Transactions:PartB
• /
• v.9B no.1
• /
• pp.7-16
• /
• 2002

The Post-clustering algorithms, which cluster the results of Web search engine, have several different requirements from conventional clustering algorithms. In this paper, we propose the new post-clustering algorithm satisfying those requirements as many as possible. The proposed Concept ART is the form of combining the concept vector that have several advantages in document clustering with Fuzzy ART known as real-time clustering algorithms. Moreover we show that it is applicable to general-purpose clustering as well as post-clustering

• Journal of Korea Multimedia Society
• /
• v.22 no.11
• /
• pp.1313-1323
• /
• 2019

With the development of the Internet and the increase of smart phones, various services considering user convenience are increasing, so that users can check news in real time anytime and anywhere. However, online news is categorized by media and category, and it provides only a few related search terms, making it difficult to find related news related to keywords. In order to solve this problem, we propose a method to recommend related documents more accurately by applying Doc2Vec similarity to the specific keywords of news articles and weighting the title and contents of news articles. We collect news articles from Naver politics category by web crawling in Java environment, preprocess them, extract topics using LDA modeling, and find similarities using Doc2Vec. To supplement Doc2Vec, we apply TF-IDF to obtain TC(Title Contents) weights for the title and contents of news articles. Then we combine Doc2Vec similarity and TC weight to generate TC weight-similarity and evaluate the similarity between words using PMI technique to confirm the keyword association.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.3
• /
• pp.219-227
• /
• 2002

The post-clustering algorithms, which cluster the result of Web search engine, have some different requirements from conventional clustering algorithms. In this paper, we propose the new post-clustering algorithm satisfying those of requirements as many as possible. The proposed fuzzy Concept ART is the form of combining the concept vector having several advantages in document clustering with fuzzy ART known as real time clustering algorithms on the basis of fuzzy set theory. Moreover we show that it can be applicable to general-purpose clustering as well as post clustering.