• The Journal of Society for e-Business Studies
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• v.9 no.3
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• pp.259-276
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• 2004

ebXML and Web Services provide UDDI and ebXML registry for storing and managing the business and Service information of companies, respectively. Recently, W3C have released the OWL(Web Ontology Language) to Recommendation, and OWL-S proposed to describe the semantics of Web Services using the OWL ontologies. In this paper, we compared the OWL-S with the registry information model(RIM) of ebXML and the data structure of UDDI, and directly connect ones, which that of ebXML similar to that of UDDI; we extend the structure of the OWL to connect the rests. Consequently, our system enables to construct the ontologies of services and discover their semantics by using the information stored in the registries, and tintegrate UDDI, ebXML registry and OWL-S registry. By using the extending OWL-S documents in our system, agents can utilize for the semantic matchmaking.

• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.111-134
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• 2005

Knowledge management is a core factor to achieve competitive advantage and improve the business performance. New information technology is also a core factor enabling the innovation of knowledge management. Semantic Web of which the goal is to realize machine-processable Web can't help affecting the knowledge management. Therefore, we empirically analyze the relationship between user's dissatisfaction and barriers or limitations of knowledge management and present methods allowing Semantic Web to overcome the limitations and to support knowledge management processes. Based on a questionnaire survey of 222 respondents, we found that the limitations of system qualities such as user inconvenience of knowledge management systems, search and integration limitations, and the limitations of knowledge qualities such as inappropriateness and untrust significantly affected the user dissatisfaction of knowledge management systems. Finally, we suggest a conceptual model of knowledge management systems of which components are resources, metadata, ontologies, and user & query layers.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.67-76
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• 2011

Recently, researches for semantic annotation methods which represent and search objects included in video data, have been briskly activated since video starts to be popularized as types for interactive contents. Different location data occurs at each frame because coordinates of moving objects are changed with the course of time. Saving the location data for objects of every frame is too ineffective. Thus, it is needed to compress and represent effectively. This paper suggests two methods; the first, ontology modeling for moving objects to make users intuitively understandable for the information, the second, to reduce the amount of data for annotating moving objects by using cubic spline interpolation. To verify efficiency of the suggested method, we implemented the interactive video system and then compared with each video dataset based on sampling intervals. The result follows : when we got samples of coordinate less than every 15 frame, it showed that could save up to 80% amount of data storage; moreover, maximum of error deviation was under 31 pixels and the average was less than 4 pixels.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.59-62
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• 2005

웹 상에서 XML 로 기술된 데이터가 증가하고, 이를 이용하여 의미 있는 데이터를 검색하는 것의 중요성이 커지고 있다. 웹 상에서 더 좋은 검색 성능을 보이기 위해 XML 질의 결과를 캐쉬하는 방법에 관한 연구들과 캐쉬의 저장 공간과 다양한 질의를 캐쉬에 저장하는 것에 한계가 있기 때문에 캐쉬 교체 기법에 관한 연구들이 있었다. 기존의 XML 캐쉬 교체 정책에는 질의 결과를 교체 단위로 하는 방법과 질의 결과 내의 각 경로들을 교체 단위로 하는 방법이 있는데, 첫번째 방법은 효율이 상대적으로 낮고 두번째 방법은 높은 효율에 비해 교체 연산을 수행하는 부담(overhead)이 크다는 단점이 있었다. 본 논문에서는 위 두 방법의 단점을 해결하기 위해 2 단계로 교체 희생자를 선택하는 방법을 제시한다. 질의 결과들 중에서 교체 희생자를 찾고, 그 희생자 내의 모든 경로들 중에서 다시 교체 희생자를 찾는다. 이는 각 질의 내의 경로가 교체 희생자가 되어 캐쉬 효율을 향상 시키고, 질의 결과에 대해 먼저 교체 대상을 찾으므로 교체 희생자를 찾기 위한 연산을 수행하는 부담을 줄인다. 또한 캐쉬 적중률, 최근 접근 시간, 인출 지연 시간, 객체 크기를 고려하여 교체 희생자를 선택하는 교체 함수를 제시한다. 가상의 시맨틱 데이터에 대한 캐쉬 교체 시스템을 구현하여 본 논문에서 제시한 교체 기법과 교체 함수를 평가한 결과를 기술한다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.1-3
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• 2012

RIF(Rule Interchange Format)는 시맨틱 웹의 구조중 규칙 계층을 담당하며 기존에 사용되고 있는 여러 상이한 규칙 언어들 간의 호환을 위한 표준 규칙 언어라고 할 수 있다. RIF는 W3C에서 승인되었다. 시맨틱웹을 위한 표준 온톨로지 언어로는 RDF와 OWL이 있으며, 최근 RDF 데이터에 대한 접근제어 (Access Control) 메커니즘과 관련하여 일부 학술적 연구가 수행되었다. 본 논문에서는 RDF 데이터와 결합될 수 있는 RIF 추론 규칙에 대해 이미 제안한 RDF 접근제어 메커니즘을 확장하고자 한다. RDF 데이터에 대해 명세된 접근 권한은 RIF 추론에 의하여 권한 충돌이 발생할 수 있고, 그로 인해 접근 권한은 허용되지 않을 수 있다. 본 논문에서는 어떤 조건에서 이러한 RIF 추론에 의한 권한 충돌이 발생하는 지를 분석하며, 이미 제안한 그래프 레이블링을 사용하는 충돌 발견 방법이 RIF 추론과 관련하여서도 효율적임을 보인다. 실험에서는 제안된 방법이, 비록 포함관계 추론에 특화 되었지만, Chase 알고리즘에 기반한 다른 연구에서의 방법보다 발견 시간을 크게 감소시킴을 보인다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.221-230
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• 2016

With emerging the Internet of Things (IoT) paradigm, the sensor network and sensor platform technologies have been changed according to exploding amount of sensors. Sensor Registry System (SRS) as a sensor platform is a system that registers and manages sensor metadata for consistent semantic interpretation in heterogeneous sensor networks. However, the SRS is unsuitable for the IoT environment. Therefore, this paper proposes sensor registry data model to register and manager sensor information in the IoT environment. We analyze Semantic Sensor Network Ontology (SSNO) for improving the existed SRS, and design metamodel based on the analysis result. We also build tables in a relational database using the designed metamodel, then implement SRS as a web application. This paper applies the SSNO and sensor ontology examples with translating into the proposed model in order to verify the suitability of the proposed sensor registry data model. As the evaluation result, the proposed model shows abundant expression of semantics by comparison with existed models.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.87-94
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• 2006

Internet makes it possible to access anytime, anywhere learning and so many LMS(Learning Management Systems) serve web based learning. But LMS has not flexible and qualified metadata to offer customired teaming. So we need extensible and flexible techniques which make if possible to define and share advanced teaming metadata. This paper presents an approach for implementing advanced learning metadata in LMS using RDF and the Semantic Web language. So we will first sketch the learning scenario in Semantic Web environment and structure of metadata management. Next we suggest two types of RDF authoring tool and search RDF documents. Advanced metadata management techniques enables the organization of learning materials around small pieces of semantically annotated learning objects. With these metadata learner can customize learning courses, improve retrieval performances.