• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.28-32
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• 2007

RUP과 ADDMe는 국내 국방 도메인에서 가장 많이 사용되는 컴포넌트 기반 소프트웨어 개 프로세스이다. ADDMe는 RUP을 기반으로 만들어졌고 국방 컴포넌트 기반 소프트웨어 개발에 표준화되었다. 국방 도메인에서 소프트웨어 재사용 증진과 소프트웨어 이해를 돕기 위해서는 RUP과 ADDMe로 개발된 프로젝트 산출물 정보들의 검색 서비스를 제공해야 한다. 또한, 기존 이미 RUP로 개발된 것과 유사한 기능을 일부 제공하는 프로젝트를 ADDMe로 개발할 경우 현재 작성중인 ADDMe 산출물과 밀접히 연관된 기존 RUP 산출물을 검색한 후 재사용할 가능성이 크다. 이를 위해서는 RUP과 ADDMe 프로세스에 존재하는 산출물들 간의 상판관계 매핑이 먼저 요구된다. 그러나 이 두 프로세스들에 존재하는 산출물 연관성 매핑 연구가 현재 존재하지 않는다. 본 논문은 위의 두 프로세스들을 분석 비교하여 온톨로지 기반 산출물 분류를 수행한다. 그리하여, 본 연구는 두 프로세스 내의 산출물들이 의미적으로 서로 어떠한 연관 관계에 있는지 어떠한 정보를 포함하는지 파악할 수 있게 하여 산출물 검색을 통한 컴포넌트 재사용을 돕는다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.469-476
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• 2018

In order to jointly utilize research infrastructure and to build efficient construction, which are essential in science and technology research and development process. Although various classification methods have been introduced for efficient utilization of registered information, functions that can be directly utilized such as similar research infrastructure search is not yet been implemented due to limitations of collection information. In this study, we analyzed the similar search technique so far, presented the methodology for the calculation of similarity of research infrastructure, and analyzed the learning result. Study suggested that a technique can be use to extract meaningful keywords from information and analyze the similarity between the research infrastructure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.623-625
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• 2003

The Semantic Web stands out in the next generation web, recently. In the Semantic Web, any information resources is defined by semantics and semantic links is given among these. It is different from existing web service environment. RDF (Resource Description Framework) is the data model to describe metadata of web resource and is to support for semantic links. And it is much the same as WSDL (Web Serice Description Language). In theis paper, we propose the RDF design method to improve the search performance by integrating RDF data unit with WSDL. We confirm the performance and efficiency of search will be improved by using the proposed method.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.101-104
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• 2000

비디오 데이터의 효율적인 검색, 요약 등에 활용하기 위해서 대용량의 비디오 데이터를 프레임(Frame), 샷(Shot),스토리(Story)의 계층적인 구조로 표현하는 방법들이 요구되고 있으며, 이에 따라 비디오를 샷, 스토리 단위로 분할하는 연구들이 수행되고 있다. 본 논문은 비디오가 샷 단위로 분할되어 있다고 가정한 후, 인접한 샷들을 결합하여 의미 있는 최소 단위인 스토리를 분할하는 방법을 제안한다. 제안하는 방법은 각 샷에서 추출된 대표 프레임들을 비교하기 위한 CCV(Color Coherence Vector) 영상 특징을 추출한다. CCV 특징의 시각적인 유사도의 초기임계값과 일정한 시간 안에 반복되는 프레임들을 찾기 위한 시간적인 유사도의 시간 임계값을 설정하여NN(Nearest Neighbor) 클러스터링 방법을 이용하여 클러스터링을 한다. 클러스터링된 정보와 같은 장면이 한번이상 반복되는 스토리의 특성을 이용해 비디오를 스토리로 분할한다. 영화 비디오 데이터를 이용한 실험을 통해 제안하는 방법의 유효성을 검증하였다.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.67-77
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• 2018

This paper suggests that LOD establishes its own link policy and publishes it to LOD cloud to provide identity among entities in different LODs. For specifying the link policy, we proposed vocabulary set founded on RDF model as well. We implemented Policy based In-depth Searching and Cleansing(PISC for short) system that proceeds in-depth searching across LODs by referencing the link policies. PISC has been published on Github. LODs have participated voluntarily to LOD cloud so that degree of the entity identity needs to be evaluated. PISC, therefore, evaluates the identities and cleanses the searched entities to confine them to that exceed user's criterion of entity identity level. As for searching results, PISC provides entity's detailed contents which have been collected from diverse LODs and ontology customized to the content. Simulation of PISC has been performed on DBpedia's 5 LODs. We found that similarity of 0.9 of source and target RDF triples' objects provided appropriate expansion ratio and inclusion ratio of searching result. For sufficient identity of searched entities, 3 or more target LODs are required to be specified in link policy.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.65-72
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• 2012

In the 21st century of information systems, the amount of information resources are ever increasing and the role of information searching system is becoming criticalto easily acquire required information from the web. Generally, it requires the user to have enough pre-knowledge and superior capabilities to identify keywords of information to effectively search the web. However, most of the users undertake searching of the information without holding enough pre-knowledge and spend a lot of time associating key words which are related to their required information. Furthermore, many search engines support the keywords searching system but this only provides collection of similar words, and do not provide the user with exact relational search information with the keywords. Therefore this research report proposes a method of offering expanded user relationship search keywords by analyzing user query patterns to provide the user a system, which conveniently support their searching of the information.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.761-766
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• 2005

This paper proposes a video model for the implementation of the cultural video database system. We have utilized an efficient scene change detection method that segments cultural video into semantic units for efficient indexing and retrieval of video. Since video has a large volume and needs to be played for a longer time, it implies difficulty of viewing the entire video. To solve this Problem. the cultural video abstraction was made to save the time and widen the choices of video the video abstract is the summarization of scenes, which includes important events produced by setting up the abstraction rule.

• 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.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.1-13
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• 2013

Recent advance and popularization of smart devices and web application services based on cloud computing have made end-users to directly produce and, at the same time, consume the image contents. This leads to demands of unified contents management services. Thus, this paper proposestag clustering method based on semantic similarity for effective image categorization. We calculate the cost of semantic similarity between tags and cluster tags that are closely related. If tags are in a cluster, we suppose that images with them are also in a same cluster. Furthermore, we could recommend tags for new images on the basis of initial clusters.