• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.75-84
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• 2005

Bulletin board systems have evolved to support diverse multimedia data as well as text. However, current board systems have an weakness : it takes much time and efforts for users to figure out contents of articles. Most board systems provide a searching function with lexical level data access for solving that problem, however it fails to serve users' intented searching results. Moreover, it is nearly impossible to search proper articles if they contain multimedia data. This paper proposed a bulletin board system adopting the Semantic Web to solve this issue. The proposed system provides users with new ontology which is used for describing articles' domain knowledge and multimedia features. Users can describe their own board ontology using the proposed ontology. To support semantic-based searching for diverse domain knowledge without modification of the system, the system dynamically generated input/query interface and RDF data access module according to the board ontology written by administrators. The proposed board system shows that semantic-based searching is feasible and effective for users to find their intended articles.

• Journal of Internet Computing and Services
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• v.7 no.2
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• pp.9-21
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• 2006

Researches on enhancing the searching function of the web service using the ontology concept have been studying. One of them suggests a searching method for UDDI using DAML and DAML+OIL. However this approach has inconveniences to use operations proper to the circumstance and to define respective ontologies according to them. To solve these problems, we introduce an effective method of dealing with N-Triple, filtering care Triples, merging Triples, semantic connection between Triples and searching Triples for searching information and recommending the results in semantic web environment. Furthermore, we implement this proposed method in a system to test it. Finally, we test the system in the virtual semantic web environment for out research analysis.

• Journal of Internet Computing and Services
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• v.4 no.3
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• pp.15-22
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• 2003

Many information storage systems, such as database system, were needed to integrate much informations into one system and to provide mare voluminous lump of informations. But as the size of information system becomes larger, the responded result size of existing keyword based searching system might be too large and couldn't do the exact search which the user intends to. In this study, we proposed a paper searching method which uses RDF semantic. For this, we analyzed the structural forms of the tit1e of research papers and reconstructed them into RDF/XML. When we use these RDF descriptions of the titles to search papers, we could get more precise and accurate results than keyword based searching method.

• Journal of Information Technology Services
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• v.7 no.3
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• pp.253-269
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• 2008

In this paper, we present an application called Semantic Search which is built on different supporting technologies and is designed to improve traditional web searching. The Semantic Search is becoming crucial challenges on semantic web. The assessment and the implementation of the research on Semantic Search is not full-fledged whereas its research is highly interested. Also there exists only little research that offers a commercial use Semantic Search System that should be taken into the account in measuring the effectiveness of a Semantic Search System. This paper proposes an implementation and evaluation for the Semantic Search System. Firstly, we built Semantic Search System which includes a case of development and it's procedure. Secondly, We presented the measurement of our Semantic Search System's effectiveness. Finally, the evaluation offers useful implications to the researchers and practitioners to improve the research level to the commercial use.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.6
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• pp.579-587
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• 2008

In collaborative environment, since each agent generally uses different words for the same meaning, there is an obstacle for information sharing. In collaborative product development environment, each agent has different words for representing same product information. As a result, it is hard to share product information in this situation. For solving this problem, semantic-based product information is needed. In this paper, a ontology-based semantic searching system which is able to interact with legacy PDM systems is proposed for product information sharing in collaborative environment Product ontology is represented with OWL format, and the product ontology is processed by Pellet reasoning engine for semantic searching. The system is implemented as a web service which can be integrated with other systems. This paper also introduces the approach with which a PDM system provides a function of semantic search with this search system.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.295-304
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• 2011

To effectively manage research information in the field of national defense, metadata about the information should be managed systematically, and an integrated system to help convergence and management of the information should be implemented based on the metadata. In addition, the system should provide the users with effective integrated search services in a mobile environment, because searching via the use of mobile devices is increasing. The objective of this paper is to suggest a MSISS (Mobile Semantic Integrated Search System), which satisfies the requirements for effective management of the national defense research information. Specifically, we defined national defense research ontologies and national defense research rules after analyzing the Dublin Core metadata and database information of the major military research institutions. We implemented a prototype system for MSISS to demonstrate the use of the ontologies and rules for semantic integrated searching of the military research information. We also presented a triple-based search service to support semantic integrated search in a mobile environment and suggested future mobile semantic integrated search services.

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

• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.29-39
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• 2006

The Web is evolving quantitatively into an explosive development. However, users usually have heavy burden of searching information because of the absence of contextual meaning on the Web. Due to an enormous amount of information, users have to endure for finding strong cohesive keywords by themselves and read each of the documents with enduring effort. This paper proposes an efficient method of searching more relative documents than current KEM-based searching systems on the Web by using contextual meaning. We designed a domain ontology on computer hardware, and a searching system which was searching those e-Learning objects. Owing to the Ontology-applied search system, information such as educational materials and related multimedia can be easily provided to the users. Further, learners could be informed of relationship of knowledge, e.g., class hierarchy, properties and values, and so on. The request results are semantically related to users' needs, and thus the system provides a learner-centered searching.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.516-523
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• 2008

Recently, the Web has been constructed based on collective intel1igence and growing up quickly. User created contents have been made the mainstream in this environments. So it's required to make an efficient technique of searching for the contents. The current searching technique mainly is achieved by key words. Semantic Web based on similarity and relationship of a language and using user tags in web2.0 also have been researched with activity. Generally, the web of the participation architecture has a lot of user created contents, various forms and classification. Therefore, it is necessary to classify and to efficiently search for a lot of user created contents. In this paper, we propose a depth searching technique considering the relationship among the tags that descript user contents. It is expected that the proposed depth searching techniques can reduce the time taken to search for the unwanted contents and the increase the efficiency of the contents searching using a service of suggestion words in tags groups.