• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.709-714
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• 2009

Recently, biological researches are required to deal with a large scale of data. While scientists used classical experimental approaches for researches in the past, it is possible to get more sophisticated observations easily with convergence of information technologies and biology. The study on diseases is one of the most important issues of the life science. Conventional services and databases provide users with information such as classification of diseases, symptoms, and medical treatments through web. However, it is hard to connect or develop them for other new services because they have independent and different criterions. It may be a factor that interferes the development of biology. In this paper, we propose an integrated data structure for the disease database, and design and implement a novel directory system for diseases as an infrastructure for developing other new services.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.113-120
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• 2010

XML is broadly using for data storing and processing. XML is specified its structural characteristic and user can query with XPath when information from data document is needed. XPath query can process when the tern and structure of document and query is matched with each other. However, nowadays there are lots of data documents which are made by using different terminology and structure therefore user can not know the exact idea of target data. In fact, there are many possibilities that target data document has information which user is find or a similar ones. Accordingly user query should be processed when their term usage or structural characteristic is slightly different with data document. In order to do that we suggest a XML document ranking method based on path similarity. The method can measure a semantic similarity between user query and data document using three steps which are position, node and relaxation factors.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.3
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• pp.143-156
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• 1998

This paper reports the GEM (Gateway to Educational Materials) project whose goal is to develop an operational framework to provide the K-12 teachers in the world with 'one-stop/any-stop' access to thousands of lesson plans, curriculum units and other Internet-based educational resources. To the IS-element Dublin Core base package, the GEM project added an 8-element, domain-specific GEM package. The GEM project employed the conceptual data modeling approach to designing the GEM database, used the Sybase relational database management system (RDBMS) to construct the backend database for storing the metadata of educational resources, and also employed the active server page (ASP) technology to provide Web interfaces to that database. The consortium members catalog lesson plans and other Internet-based educational resources using a cataloging module program that produces HTML meta tags. A harvest program collects these meta tags across the Internet and outputs an ASCII file that conforms to the standard agreed by the consortium members. A parser program processes this file to enter meta tags automatically into appropriate relational tables in the Sybase database. The conceptual/logical schemas of Dublin Core and GEM profile are presented. The advantages of conceptual modeling approach to manage metadata are discussed. A prototype system that provides access to the GEM metadata is available at http://lis.skku.ac.kr/gem/.

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.163-178
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• 2005

The ultimate objective of Personal Information Management (PIM) is to collect, handle and manage wanted information in a systematic way that enables individuals to search the information more easily and effectively, However, existing personal information management systems are usually based on a traditional hierarchical directory model for storing information, limiting effective organization and retrieval of information as well as providing less support in search by associative interrelationship between objects (documents) and their attributes, To improve these problems, in this paper we propose a personal information management model based on the concept lattice of Formal Concept Analysis (FCA) to easily build and maintain individuals' own information on the Web, The proposed system can overcome the limitations of the traditional hierarchy approach as well as supporting search of other useful information by the inter-relationships between objects and their attributes in the concept lattice of FCA beyond a narrow search.

• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.127-134
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• 2015

In this paper, we design a semantic-based syllabus template including learning ontologies. A syllabus has been considered as a important blueprint of teaching in universities. However, the current syllabus has no importance in real world because most of all syllabus management systems provide simple functionalities such as, creation, modification, and retrieval. In this paper, our approach consists of definition of hierarchical structure of syllabus and semantic relationships of syllabuses, formalization of learning goals, learning activity, and learning evaluation using Bloom's taxonomy and design of learning subject ontologies for improving the usability of syllabus. We prove the correctness of our proposed methods according to implementing a real syllabus for JAVA programing course and experiments for retrieving syllabuses.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.63-69
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• 2015

There are various researches which proposed an automatic instance generation from freetext on the web. Existing researches that focused on English, adopts pattern representation which is generated by simple rules and regular expression. These simple patterns achieves high performance, but it is not suitable in Korean due to differences of characteristics between Korean and English. Thus, this paper proposes a novel method for generating patterns and instances which focuses on Korean. A proposed method generates high quality patterns by taking advantages of dependency relations in a target sentences. In addition, a proposed method overcome restrictions from high degree of freedom of word order in Korean by utilizing postposition and it identifies a subject and an object more reliably. In experiment results, a proposed method shows higher precision than baseline and it is implies that proposed approache is suitable for self-knowledge learning system.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.27-34
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• 2016

The referents of internet addresses are no longer limited to web sites. A new address service by the international organization for the internet address (ICANN) introduces an open space for the TLD (Top Level Domain) strings so that each service, content, product, and other linguistic expressions may be allowed. The open TLD addresses are more suitable for representing the address of service units, contents, or products. In this paper, as an alternative to static Internet address service to return a consistent mapping result regardless of a user-specific different requirements, we design a dynamic internet address mapping model that returns mapping result to suit user particular requirements. In addition, we propose a method for implementing a internet address service without any changes in the existing domain protocols. It may implement a dynamic internet address by attaching to a encoded user's metadata and environment data within a internet address representation, and adding the module for dynamic mapping to the name servers. Through this proposal, trying to expand the functions of internet address, it is expected to be able to provide customized informaiton retrieval services for each user by using just internet address.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.71-87
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• 2007

With the rapid change of the Web and E-transaction application, the search interface is providing more powerful search and visualization methods, while offering smoother integration of technology with task. Especially, visualization techniques for long menu-lists are applied in retrieval system with the goal of improving performance in user's ability to select one item from a long list. In order to review visualization techniques appropriate to the types of users and data set, this study compared the five visualization browsers such as the Tree-structured menu, the Table-of-contents menu, the Roll-over menu, the Click menu, and Fisheye menu. The result of general analyses shows that among the hierarchical methods, the experienced group prefers the Table-of-contents method menu, whereas the novice's group prefers the Tree-structure method menu. Among the linear methods, the two groups prefer the Roll-over menu. The Roll-over menu is most preferred among the five browsers by the two groups.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.306-319
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• 2009

A name is a key feature for distinguishing people, but we often fail to discriminate people because an author may have multiple names or multiple authors may share the same name. Such name ambiguity problems affect the performance of document retrieval, web search and database integration. Especially, in bibliography information, a number of errors may be included since there are different authors with the same name or an author name may be misspelled or represented with an abbreviation. For solving these problems, it is necessary to disambiguate the names inputted into the database. In this paper, we propose a method to solve the name ambiguity by using social networks constructed based on the relations between authors. We evaluated the effectiveness of the proposed system based on DBLP data that offer computer science bibliographic information.

• 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.