• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1093-1102
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• 2003

As the use of internet is growing, the amount of information is increasing rapidly and XML that is a standard of the web data has the property of flexibility of data representation. Therefore electronic document systems based on web, such as EDMS (Electronic Document Management System), ebXML (e-business extensible Markup Language), have been adopting XML as the method for exchange and standard of documents. So research on the method which can manage and search structural XML documents in an effective wav is required. In this paper we propose the clustering method based on structural similarity among the many XML documents, using typical structures extracted from each document by sequential pattern mining in pre-clustering process. The proposed algorithm improves the accuracy of clustering by computing cost considering cluster cohesion and inter-cluster similarity.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.1-8
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• 2007

As the web document of XML, an exchange language of data in the advanced Internet, is increasing, a target of information retrieval becomes the web documents. Therefore, there we researches on structure, integration and retrieval of XML documents. This paper proposes a clustering method of XML documents based on frequent structures, as a basic research to efficiently process query and retrieval. To do so, first, trees representing XML documents are decomposed and we extract frequent structures from them. Second, we perform clustering considering the weight of large items to adjust cluster creation and cluster cohesion, considering frequent structures as items of transactions. Third, we show the excellence of our method through some experiments which compare which the previous methods.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.393-398
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• 2001

특정 영역에 대해 사용자에게 관련 정보를 제공해 주는 서비스를 하는 정보 에이전트를 개발 중이다. 정보 에이전트는 사용자 질의 처리를 달은 Agent Manager와 지식베이스를 관리하는 KB Manager, 그리고 Web으로부터 해당 영역의 관련 문서를 끌어오는 Web Manager로 구성되어 있다. Web Manager는 방문할 URL을 수집하고, 이들 문서에 대한 관련 평가와 Indexing을 수행한다. Web Manager는 검색 엔진을 이용하거나, 방문한 문서의 link를 이용하여 URL을 수집하는데 이러한 URL수집기법은 많은 관련 문서를 놓치는 문제점이 있다. 이 문제점을 해결하기 위해서 해당 영역과 관련된 Site들을 대상으로 Link를 이용해 문서들을 모아와, 문서들을 TAG들의 패턴으로 얻어낸 문서 형식을 이용해 Clustering하며 관련 문서들의 Group을 찾아내는 적극적 문서 수집 기법을 제안한다. 실험 결과, Link와 Clustering을 이용할 경우 기존보다 효과적으로 관련 문서를 많이 수집할 수 있음을 알 수 있다.

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

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.55-61
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• 2005

We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.29-36
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• 2010

Past clustering researches are focused on extraction of keyword for word similarity grouping. However, too many candidates to compare and compute bring high complexity, low speed and low accuracy. To overcome these weaknesses, this paper proposed a topical web document clustering model using not only keyword but also named entities such as person name, organization, location, and so on. By several experiments, we prove effects of our model compared with traditional model based on only keyword and analyze how different effects show according to characteristics of document collection.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.53-62
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• 2012

Clustering technology is used to deal efficiently with many searched documents in information retrieval system. But the accuracy of the clustering is satisfied to the requirement of only some domains. This paper proposes two methods to increase accuracy of the clustering. We define a common-word, that is frequently used but has low weight during clustering. We propose the method that automatically gathers the common-word and calculates its weight from the searched documents. From the experiments, the clustering error rates using the common-word is reduced to 34% compared with clustering using a stop-word. After generating first clusters using average link clustering from the searched documents, we propose the algorithm that reevaluates the similarity between document and clusters and reclassifies the document into more similar clusters. From the experiments using Naver JiSikIn category, the accuracy of reclassified clusters is increased to 1.81% compared with first clusters without reclassification.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.73-80
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• 2008

In this paper, an advertisement-keyword finding method using document clustering is proposed to solve problems by ambiguous words and incorrect identification of main keywords. News articles that have similar contents and the same advertisement-keywords are clustered to construct the contextual information of advertisement-keywords. In addition to news articles, the web page and summary of a product are also used to construct the contextual information. The given document is classified as one of the news article clusters, and then cluster-relevant advertisement-keywords are used to identify keywords in the document. We could achieve 21% precision improvement by our proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2335-2342
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• 2006

The current web is accumulating abundant information. In particular, text based documents are a type used very easily and frequently by human. So, numerous researches are progressed to retrieve the text documents using many methods, such as probability, statistics, vector similarity, Bayesian, and so on. These researches however, could not consider both subject and semantic of documents. So, to overcome the previous problems, we propose the document similarity method for semantic retrieval of document users want. This is the core method of document clustering. This method firstly, expresses a hierarchy semantically of document content ut gives the important hierarchy domain of document to weight. With this, we could measure the similarity between documents using both the domain weight and concepts coincidence in the domain hierarchies.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.389-397
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• 2017

Recently semantic web document is produced and added in repository in a cloud computing environment and requires an intelligent semantic agent for analytical classification of documents and information retrieval. The traditional methods of information retrieval uses keyword for query and delivers a document list returned by the search. Users carry a heavy workload for examination of contents because a former method of the information retrieval don't provide a lot of semantic similarity information. To solve these problems, we suggest a key word frequency and concept matching based semantic clustering model using hadoop and NoSQL to improve classification accuracy of the similarity. Implementation of our suggested technique in a cloud computing environment offers the ability to classify and discover similar document with improved accuracy of the classification. This suggested model is expected to be use in the semantic web retrieval system construction that can make it more flexible in retrieving proper document.