• Journal of KIISE
• /
• v.41 no.9
• /
• pp.686-698
• /
• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• Journal of the Korean Society for information Management
• /
• v.21 no.1
• /
• pp.187-207
• /
• 2004

Recently there has been a growing interest in the investigation and development of the next generation web - the Semantic Web. From the perspective of a information science, the next generation web - Semantic Web is a metadata initiative. It is reason that one of important stage of Semantic Web Construction is adding formal metadata that describes a Web resource's content and so people can find easy material using metadata. In this paper, 1 designed new application profile metadata architecture as a way to serve as interoperability between various open digital libraries using different information architecture in Semantic Web environment. Based on new application profile metadata architecture, 1 developed union metadata automatic generation and union search algorithm to integrate heterogeneous huge-scale metadata in the open digital library.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.6
• /
• pp.2022-2042
• /
• 2014

The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance today's big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data.

• Cross-Cultural Studies
• /
• v.31
• /
• pp.287-308
• /
• 2013

Russian language tends to be quite open to borrowing. In Russian it has been for a long time the conventional way of expanding the lexicon, accepting many words from adjacent languages, including Church Slavic. In the contemporary Russian English has been the main source for loanwords. There are several linguistic factors for lexical borrowing: 1. the necessity of denominating new facts, phenomena or concepts, 2. the necessity of differentiating concepts, 3. the necessity of specializing new concepts, 4. the introduction of new international terms, 5. the increase of periphrastic expressions, 6. the needs for the more elegant and modern words. These factors have caused borrowing to enlarge the component of the lexicon and phrasal expressions, but excessive use of foreign words has brought about negative effects such as linguistic pollution. Some borrowed words are assimilated without serious conflicts, but other words undergo semantic changes in confrontation to existing words of similar meanings. These types of semantic changes comprise total change of meaning, reduction of semantic scale and extension of meaning. Semantic changes are caused by linguistic factors such as lexical conflict with existing words or by socio-culural factors such as misunderstanding of foreign words. And extension of meaning shows two types: qualitative extension and quantitative extension. The first means extending the semantic scope of a borrowed word and the latter - increasing the number of its sememe. In contemporary Russian language we can witness two productive phenomena: qualitative extension by socio-cultural factors, in which words with negative nuances are changed into those with positive ones and professional terms become common words, losing their professional meanings. On the other hand, by quantative extension some loanwords change their concrete meanings into abstract ones. In such cases loanwords acquire the additional meanings of abstractness, putting aside their original concrete meanings as the basic. On the contrary, the qualitative extension of adding the special meaning to general words or giving the concrete meaning to abstract words is not productive. And it is rarely witnessed that words of positive nuances are negatively used. It is considered that such cases are partly restricted in the spoken language or the jargon. Such phenomena may happen by the incomplete understanding of English words.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.105-109
• /
• 2010

While numerous information services are provided on the Web as a core infrastructure of information society, semantic services using the Semantic Web technologies still stay deployed number and application range. This situation would be mainly originated from the failure of securing reliability to the user. Thus, this paper introduces an evaluation method for measuring reliabilities of the semantic services comparatively. To measure the reliability of the compared systems, the observer assesses 'precision in task performance' as a quantitative analysis and 'reliability of expectation-result' as a qualitative analysis described by the test persons. On the other hand, the test person should rate the functional reliability and reliability of the served information on the vector graph by himself with a scale from 0 to 5. Experimental results show that assessment by the observer is very similar to rating value by test persons, and that the accuracy of the served information has a close effect on the functional reliability. Through this paper, we can verify the essential factors for evaluating the reliability of semantic service systems. These are functional reliability and reliability of served information resulting from function execution. In particular, it has been proven that the reliability of the semantic information services largely influences the "Quality in Use" and therefore determines the major factors of the semantic service reliability.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2000.04a
• /
• pp.426-430
• /
• 2000

This study is about the human sensibility measurement method. Researches on human sensibility measurement have been performed in many aspects. However, due to the subtlety and intricacy of human sensibility, it has been difficult to exactly measure human sensibility.At present, Semantic Differential(SD) method is most widely used to quantify human sensibility. In SD method, complicate human sensibility is represented on a scale with equal interval or equal ratio. However, this method has a critical problem in that human sensibility cannot be expressed with equal interval or equal ratio.In order to complement SD method and represent human sensibilities appropriately, we proposed an alternative method using the chroma-scale with saturation that had not equal interval or ratio. In experiment, proposed method shown that subjects understood a new scale easily and they could express their sensibilities on a new scale without any limitation. As a result, the chroma-scale can be used not only for measuring consumer's preference but also for evaluating or selecting sensibility words.

• Journal of Korean Society for Geospatial Information Science
• /
• v.17 no.1
• /
• pp.121-129
• /
• 2009

Today, it is difficult to re-use and share spatial information, because of the explosive growth of heterogeneous information and specific characters of spatial information accumulated by diverse local agency. A spatial analysis of subsurface spatial informa-tion, one of the National Spatial Data Infrastructure, needs related spatial information such as, topographical map, geologic map, underground facility map, etc. However, current methods using standard format or spatial datawarehouse cannot consider a se-mantic hetergenity. In this paper, the layered ontology model which consists of generic concept, measuremnt scale, spatial model, and subsurface spatial information has developed. Also, the current ontology building method pertained to human experts is a expensive and time-consuming process. We have developed the MDA-based metamodel(UML Profile) of ontology that can be a easy under-standing and flexiblity of environment change. The semantic quality of devleoped ontology model has evaluated by reasoning engine, Pellet. We expect to improve a semantic sharing, and strengthen capacities for developing GIS experts system using knowledge representation ability of ontology.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.325-334
• /
• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of Korean Academy of Nursing Administration
• /
• v.13 no.4
• /
• pp.509-515
• /
• 2007

Purpose: The purpose of this study was to translate and validate the Korean version of Hall's Professional Inventory(HPI) scale to assess levels of professionalism among Korean registered nurses. Method: The 25 item HPI scale was translated and content review was done by translation panel. After the content review, a bilingual nursing scholar performed the back-translation into English. A semantic equivalence test was conducted with 5 American nursing professors. A pilot study was conducted with a sample of 164 registered nurses in Korea to test the validity and reliability of the translated HPI. Result: The content equivalence for translated version of HPI was validated by a translation panel. The finding of the semantic equivalence test of back-translated version was 72.8%. The Cronbach's alpha for the Korean version of HPI was .820. Conclusions: This study provides information about the issues of translating an instrument such as the HPI. The Korean version of the HPI is a valid and reliable instrument and can have psychometric properties equivalent to those of the original HPI. The translated version could be used for assessing levels of professionalism for other health care professionalism as well as nurses.

• Fibers and Polymers
• /
• v.2 no.4
• /
• pp.196-202
• /
• 2001

In order to investigate the relationship between subjective sensation for fabric sound and touch and the objective measurements, eight different apparel fabrics were selected as specimens. Sound parameters of fabrics including level pressure of total sound (LPT), level range (ΔL), and frequency differences (Δf) and mechanical properties by Kawabata Evaluation System (KES) were obtained. For subjective evaluation, seven aspects of the sound (softness, loudness, pleasantness, sharpness, clearness, roughness, and highness) and eight of the tough (hardness, smoothness, fineness, coolness, pliability, crispness, heaviness, and thickness) were rated using semantic differential scale. Polyester ultrasuede was evaluated to sound softer and more pleasant while polyester taffeta to sound louder and rougher than any other fabrics. Wool fabric such as worsted and woolen showed similar sensation for sound but differed in some touch sensation in that woolen was coarseast, heaviest, and thickest in touch. In the prediction model for sound sensation, LPT affected positively subjective roughness and highness as well as loudness, while ΔL was found as a parameter related positively with softness and pleasantness. Touch sensation was explained by some of mechanical properties such as surface, compressional, shear, and bending properties implying that a touch sensation could be expressed by a variety of properties.