• ETRI Journal
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• v.29 no.6
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• pp.794-807
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• 2007

In the mobile Internet, users generally work with handheld devices with limited computing power and small screens. Their access conditions also change frequently. In this paper, we present a novel method supporting intelligent content adaptation to better suit handheld devices. The underpinning is a unit of information (UOI)-based content adaptation method, which automatically detects semantic relationships among the components of Web contents and then reorganizes page layout to fit handheld devices based on identified UOIs. Experimental results demonstrate that our method enables more sensitive content adaptation.

• Journal of the Ergonomics Society of Korea
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• v.17 no.1
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• pp.55-65
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• 1998

This paper describes study of design specifications of restaurant/cafe signboard for new generation. Signboard is an important element in restaurant/cafe, especially for sensitive new generation, that affects image of and first impression on it. The design elements of a signboard that were chosen for analysis were background color, lettering, and letter color, which were found to have strong influences on impression of signboard in questionnaire survey. Using different combination of these design elements, fifteen samples were created for subjective evaluation. 30 pairs of adjectives which were shown to be related to signboard significantly in the first evaluation were used by fifteen subjects, twelve men and three women. Subjective evaluations were carried out by semantic differential methods, and then analyzed by using multivariate analyses. Among three design elements, the background color affected impression of signboard most significantly. The relationships between design elements (item/category) and subjective impressions were suggested using quantification theory I, which could be used as design guidelines when designing restaurant/cafe signboard with specific human sensibility ergonomic characteristics.

• ETRI Journal
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• v.34 no.4
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• pp.591-601
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• 2012

Unstructured peer-to-peer (p2p) networks usually employ flooding search algorithms to locate resources. However, these algorithms often require a large storage overhead or generate massive network traffic. To address this issue, previous researchers explored the possibility of building efficient p2p networks by clustering peers into communities based on their social relationships, creating social-like p2p networks. This study proposes a social relationship p2p network that uses a measure based on Hebbian theory to create a social relation weight. The contribution of the study is twofold. First, using the social relation weight, the query peer stores and searches for the appropriate response peers in social-like p2p networks. Second, this study designs a novel knowledge index mechanism that dynamically adapts social relationship p2p networks. The results show that the proposed social relationship p2p network improves search performance significantly, compared with existing approaches.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.873-885
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• 2015

Today, It is difficult to search the various and numerous information efficiently. For this reason, Semantic Web emerged to provide searching services more easily through the structuring of a variety of unstructured format data and the definition of meaningful relationships between information. Especially, definition of relationship and meaning among resources is significant to share and infer related information. Ontology modeling plays just that role. Weapon parts development information is unstructured and dispersed all over. There are many difficulties in finding desired information, leading to getting improper outcomes. In this paper, we present an intuitive ontology model with weapon parts development information including the multi-dimensional information analysis and expansion of the relevant information. This study build up a ontology model through creating class and hierarchy about parts information and defining the properties of classes with Ontology Development 101[1] procedures using Protégé tools. The ontology model provides users with a platform on which search of needed information can be easy and efficient.

• Journal of the Korean Society for information Management
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• v.1 no.1
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• pp.78-99
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• 1984

In this paper, the automatic keyword classification which is one of the automatic construction methods of retrieval thesaurus is experimented to the Korean language on the basis that the use of retrieval thesaurus would increase the efficiency of information retrieval in the natural language retrieval system searching machine-readable data base. Furthermore, this paper proposes the application methods. In this experiment, the automatic keyword classification was based on the assumption that semantic relationships between terms can be found out by the statistical patterns of terms occurring in a text.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.766-771
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• 2006

In this paper, we focus on the building domain ontology from corpus by extracting concepts and properties relationships based on linguistic patterns. The pharmacy field is selected as an experiment domain and we present an algorithm to extract hierarchical structure for terminology based on the noun/suffix patterns of terminology in domain texts. In order to show usefulness of our domain ontology, we compare a typical keyword based retrieval method with an ontology based retrieval mettled which uses related information in an ontology for a related feedback. As a result, our method shows the improvement of precision by 4.97% without losing recall.

• Korean Journal of Occupational Health Nursing
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• v.30 no.3
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• pp.132-143
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• 2021

Purpose: The study aimed to explore experiences of the posttraumatic growth (PTG) in firefighters with repeated exposure to traumatic events. Methods: Participants were 11 firefighters from two fire departments, who had experienced more than one critical trauma events. Data were collected through personal interviews from August to October 2020 and analyzed by Colaizzi's phenomenological methods. Results: The PTG experiences were derived into four categories: 'growth in self-perception', 'rediscovery of the meaning of life', 'deep interpersonal relationships', and 'discovery of the meaning of work'. Conclusion: These findings could be used as basic information for developing PTG program for firefighters such as logo-therapy, semantic therapy, and self-disclosure intervention using expressive writing and speaking.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1389-1391
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• 2011

In this paper, we propose a methodology to annotate the digital documents through keyphrase extraction using domain specific taxonomy. Limitation of the existing keyphrase extraction algorithms is that output keyphrases may contain irrelevant information along with relevant ones. The quality of the generated keyphrases by the existing approaches does not meet the required level of accuracy. Our proposed approach exploits semantic relationships and hierarchical structure of the classification scheme to filter out irrelevant keyphrases suggested by Keyphrase Extraction Algorithm (KEA++). Our experimental results proved the accuracy of the proposed algorithm through high precision and low recall.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.881-888
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• 2005

As the internet continues to grow, the quantity of information on the Web increases beyond measure. The internet users' abilities and requirements to use information also become varied and complicated. Ontology can describe correct meaning of web resource and relationships between web resources. And it can extract conformable information that a user wants. Accordingly, we need the ontology to represent knowledge. W3C announced OWL(Web Ontology Language), a meaning description technology for such web resources. But, the development of a professional use of tools that can compose and edit effectively is not yet developed adequately. In this paper, we design and implement an Ontology editor which generates and edits OWL documents through intuitional interface, with a OWL parser, a Internal DataModel, and a Serializer.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.125-135
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• 2014

In the medical field, for the purpose of diagnosis and treatment of diseases, building knowledge base has received a lot of attention. The most important thing to build a knowledge base is representing the knowledge accurately. In this paper we suggest a knowledge representation method using Ontology technique with the datasets obtained from the domestic papers on Alzheimer disease that has received a lot of attention recently in the medical field. The suggested Ontology for Alzheimer disease defines all the possible classes: lexical information from journals such as 'author' and 'publisher' research subjects extracted from 'title', 'abstract', 'keywords', and 'results'. It also included various semantic relationships between classes through the Ontology properties. Inference can be supported since our Ontology adopts hierarchical tree structure for the classes and transitional characteristics of the properties. Therefore, semantic representation based query is allowed as well as simple keyword query, which enables inference based knowledge query using an Ontology query language 'SPARQL'.