• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.1-10
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• 2017

Open data has received a lot of attention from around the world. The Korean government is also making efforts to open government data. However, despite the quantitative increase in public data, the lack of data is still pointed out. This paper proposes a method to improve data sharing and utilization by semantically linking public data. First, we propose a knowledge model for expressing administrative districts and their semantic relationships in Korea. An administrative district is an administrative unit that divides the territory of a nation, which is a unit of politics, according to the purpose of the state administration. The knowledge model of the administrative district defines the structure of the administrative district system and the relationship between administrative units based on the Local Autonomy Act. Second, a knowledge graph of the administrative districts is introduced. As a reference information to link public open data at a semantic level, some characteristics of a knowledge graph of administrative districts and methods for linking heterogeneous public open data and improving data quality are addressed. Finally, some use cases are addressed for interlinking between the knowledge graph of the administrative districts and public open data. In particular, national administrative organisations are interlinked with the knowledge graph, and it demonstrates how the knowledge graph can be utilised for improving data identification and data quality.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.119-134
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• 2018

A knowledge map as the taxonomy used in a knowledge repository should be structured to support and supplement knowledge activities of users who sequentially inquire and select knowledge for problem solving. The conventional knowledge map with a hierarchical structure has the advantage of systematically sorting out types and status of the knowledge to be managed, however it is not only irrelevant to knowledge user's process of cognition and utilization, but also incapable of supporting user's activity of querying and extracting knowledge. This study suggests a methodology for constructing a networked knowledge map that can support and reinforce the referential navigation, searching and selecting related and chained knowledge in term of contents, between knowledge. Regarding a keyword as the semantic information between knowledge, this research's networked knowledge map can be constructed by aggregating each set of knowledge links in an automated manner. Since a keyword has the meaning of representing contents of a document, documents with common keywords have a similarity in content, and therefore the keyword-based document networks plays the role of a map expressing interactions between related knowledge. In order to examine the feasibility of the proposed methodology, 50 research papers were randomly selected, and an exemplified networked knowledge map between them with content relevance was implemented using common keywords.

• Journal of The Korean Association of Information Education
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• v.11 no.3
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• pp.317-327
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• 2007

The purpose of this study is to consider ontology theory and get to forge design and implementation of ontology-based access system which is support to the nutrition and food guide tower of Home Economics textbooks in middle school in order to offer the way of effective learning performance. It offers a model by establishing a nutrition and food guide tower access system based on Protege-2000 framework. This system is on the basis of XML, and it makes possible to work with semantic web, a next generation internet technology, and provides a meaning structure that can be shared in the field of nutrition in order to build up the fundament of knowledge an information system for the mutual operations. A learner can systemize the knowledge through a self-information access and an instructor can also check out the degree of learner's learning-accomplishment and interests, directly putting the access system into the teaching and learning process. In addition, it is supposed that the learner can maintain a balance and healthy life by internalizing his or her knowledge throughout ontology not only in a teaching and learning process but also in a daily life.

• Journal of Technology Innovation
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• v.21 no.1
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• pp.279-301
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• 2013

This study demonstrates how social network analysis can be used for identifying potential buyers in technology marketing; in such, the methodology and empirical results are proposed. First of all, we derived the three most important 'seed' keywords from 'technology description' sections. The technologies are generated by various types of R&D activities organized by South Korea's public research institutes in the fundamental science fields. Second, some 3, 000 words were collected from websites related to the three 'seed' keywords. Next, three network matrices (i.e., one matrix per seed keyword) were constructed. To explore the technology network structure, each network is analyzed by degree centrality and Euclidean distance. The network analysis suggests 100 potentially demanding companies and identifies seven common companies after comparing results derived from each network. The usefulness of the result is verified by investigating the business area of the firm's homepages. Finally, five out of seven firms were proven to have strong relevance to the target technology. In terms of social network analysis, this study expands its application scope of methodology by combining semantic network analysis and the technology marketing method. From a practical perspective, the empirical study suggests the illustrative framework for exploiting prospective demanding companies on the web, raising possibilities of technology commercialization in the basic research fields. Future research is planned to examine how the efficiency of process and accuracy of result is increased.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.949-969
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• 2014

The purpose of this study is to analyze the share of global citizenship education in the 2009 Revised Social Studies (geography area) School Curriculum of the Republic of Korea. I selected the achievement standards of the geography domain in the fifth and sixth grades as the subjects of analysis. The chosen subjects were examined using content analysis: I used KrKwic, a Korean language content analysis tool, to analyze the content and drew a semantic network of the analysis results using UciNet/NetDraw. I found that the geography domain of the 2009 Revised Primary School Curriculum included the concepts of and factors of global citizenship education. However, global citizenship education did not account for a major portion of the curriculum, and the curriculum achievement standards were noticeably nation-state centered. Global citizenship education factors were not closely associated with to other related factors in fact, they even revealed a isolated pattern. These findings suggest that the inclusion of global citizenship education in primary geography education is limited, because the connections between global citizenship education and related contents, such as the environment, sustainable development, conflict, and cooperation, are probably impeded. Globalization accompanies the transformation of territories, identities, and the relations between nation-states and the world, although nation-states continue to play a significant role in the globalized worlds. Therefore global citizenship education, a educational trend focusing on the global community, is particularly important and is required in the geography curriculum of the global era. I expect that the examination undertaken in this study to contribute to future curriculum revisions regarding globalizatin and global citizenship.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.6
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• pp.84-96
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• 2008

This research investigated the landscape characteristics of the skyline and the cognitive characteristics of Mt. Mudeung (1,186m) from various viewpoints. Mt. Mudeung, the representative landscape of Gwangju City, has been recognized as a natural landmark and theme of paintings. By analyzing the perspective from 32 points with a digital terrain model, some landscape indices of the skyline were derived and the relationships are discussed. Assessment of the semantic differential scale with 21 adjective variables and representativeness to 15 landscape photographs of the mountain were accomplished. 1. Through regression analysis of the skyline indices, significant relationships were found between them the angle from the visual axis and number of skyline jumps, the vertical angle fluctuation and number of jumps per degree, the visual depth fluctuation and vertical angle fluctuation of skyline, and between the vertical angle mean and number of jumps per degree. Meaningful relations were found between the number of jumps of skyline to number of jumps per degree and the angle from visual axis to visual distance. However, in the representative assessment no difference was found on the angle from visual axis of viewpoints. On the other hand, it seemed to relate representativeness with visual clarity based on visual distance. 2. We found 4 factors "familiarity", "fluctuation of skylines", "openness", and "feeling of texture" in the results of factor analysis of semantic differential assessment. When considering the results of assessment for representativeness, adjective words for familiarity and openness seemed to have a close assessment. Specifically, the research showed that the landscape representation was highly assessed in a view which could be seen from the higher parts to the lower part of hills. This result indicates that the management of viewpoints which could get a scene from intermediate to distant, and locating a high elevation is important. 3. In the picturesque expression of Mt. Mudeung, various impressions from the different points, a skyline based on the top of Mt. Mudeung and a mono structure by overlapping hills were common characteristics. These common characteristics were also partially found through the analysis of topographical landscape indices and landscape images. Therefore, the viewpoints for the representative landscape management should be selected in natural or open spaces.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.676-683
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• 2002

The success of XML(eXtensible Markup Language) is primarily based on its flexibility : everybody can define the structure of XML documents that represent information in the form he or she desires. XML is so flexible that XML documents cannot be automatically provided with an underlying semantics. Different tag sets, different names for elements or attributes, or different document structures in general mislead the task of classifying and clustering XML documents precisely. In this paper, we design and implement a system that allows checking the semantic-based similarity between XML tags. First, this system extracts the underlying semantics of tags and then expands the synonym set of tags using an WordNet thesaurus and user-defined word library which supports the abbreviation forms and compound words for XML tags. Seconds, considering the relative importance of XML tags in the XML documents, we extend a conventional vector space model which is the most generally used for document model in Information Retrieval field. Using this method, we have been able to check the similarity between XML tags which are represented different tags.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.175-181
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• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.