• Journal of the Korean Society for Library and Information Science
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• v.41 no.4
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• pp.229-250
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• 2007

This paper suggested a way to solve the problems by using domain experts who are already in the significant level of knowledge in those fields. For the purpose of achieving our goal, a very simple and efficient approach to construct the knowledge-base which can play an important role in providing researchers with essential information in need was proposed. In addition, the Adaptive R&D Information Service Portal with a new schema structure and a construction method of representing expert's knowledge efficiently was developed. With the simplicity and expandability of the proposed system it can be a good model for a similar system to be developed.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.103-108
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• 2008

주제어는 정보 서비스를 비롯한 여러 응용 분야들에서 유용하게 사용되는 지식이지만, 주제어 간 관계가 다양할 뿐만 아니라 목적에 맞도록 개별적으로 설계됨으로써 주제어 관계 속성 유형과 무관하게 공유가 가능한 주제어 공통 표현 모델이 제시되지 못하였다. 본 연구는 응용 분야, 온톨로지 종류와 무관하게 적용될 수 있으며 시맨틱 웹 서비스 간 공유가 가능한 주제어 공통 표현 모델을 제시하고자 한다. 이를 위해, 주제어 관계를 범용 클래스로 정의하고, 주제어 관계 속성 유형을 데이터타입 속성 (Datatype Property)으로 선언하였다. 또한, 주제어 역시 그 속성 유형을 데이터타입 속성으로 선언하였는데, 결국 다양한 유형의 관계들을 용이하게 표현할 수 있도록 하기 위한 것이다. 실험을 위해 주제어 간 관계수가 70,804,233개이며 주제어 관계 속성 유형이 4가지인 과학 기술 기반 정보 온톨로지와 주제어 간 관계수가 44,147개이며 주제어 관계 속성 유형이 13가지인 표준 정보 온톨로지를 대상으로 본 연구에서 제안한 주제어 공통 표현 모델을 적용하였으며 총 284,744,802개의 RDF(Resource Description Framework) Triple을 생성하였다.

• Transactions of Materials Processing
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• v.17 no.6
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• pp.393-400
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• 2008

Springback is one of the most difficult phenomena to analyze and control in sheet forming. Most of traditional springback control methods rely on experiences of skilled workers in industrial fields. This study focuses on prediction and generation of optimum reconfigurable die surfaces to control shape errors originated by springback. For this purpose, a deformation transfer function(DTF) was combined with finite element analysis of the springback in the 2D sheet forming model of elastic-perfectly plastic materials under the condition without blank holder. The results showed shape errors within 1% of the objective shape, which were comparable with analytically predicted errors. In addition to this theoretical analysis, DTF method was also applied to 2D and 3D sheet forming experiments. The experimental results showed ${\pm}0.5$ mm and ${\pm}1.0$ mm shape error distribution respectively, demonstrating that reconfigurable die surfaces were predicted well by the DTF method. Irrespective of material properties and sheet thickness, the DTF method was applicable not only to FEM simulation but also to 2D and 3D elasto-reconfigurable die forming. Consequently, this study shows that springback can be controlled effectively in the elasto-RDF system by using the DTF method.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Smart Media Journal
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• v.3 no.3
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• pp.20-25
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• 2014

This research proposes a concept of multi-aspect Social IoT platform that enables human, machine and service to communicate smoothly among them, as well as a means of an automatic process for transforming exiting domain knowledge representation to generic ontology representation used in the platform. Current research focuses on building a machine-based service interoperability using sensor ontology and device ontology. However, to the best of our knowledge, the research on building a semantic model reflecting multi-aspects among human, machine, and service seems to be very insufficient. Therefor, in the research we first build a multi-aspect ontology schema to transform the representation used in each domain as a part of IoT into ontology-based representation, and then develop an automatic process of generating multi-aspect IoT ontology from the domain knowledge based on the schema.

• Journal of the Korean Society for information Management
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• v.27 no.3
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• pp.35-51
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• 2010

Emerging Web 2.0 services such as Twitter, Blogs, and Wikis alongside the poorlystructured and immeasurable growth of information requires an enhanced information organization approach. Ontology has received much attention over the last 10 years as an emerging approach for enhancing information organization. However, there is little penetration into current systems. The purpose of this study is to propose ontology implementation and methodology. To achieve the goal of this study, limitations of traditional information organization approaches are addressed and emerging information organization approaches are presented. Two ontology data models, RDF/OW and Topic Maps, are compared and then ontology development processes and methodology with topic maps based medical information retrieval system are addressed. The comparison of two data models allows users to choose the right model for ontology development.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.390-401
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• 2014

ISO 15926 is an international standard for integration of lifecycle data for process plants including oil and gas facilities. From the viewpoint of information modeling, ISO 15926 Parts 2 provides the general data model that is designed to be used in conjunction with reference data. Reference data are standard instances that represent classes, objects, properties, and templates common to a number of users, process plants, or both. ISO 15926 Parts 4 and 7 provide the initial set of classes, objects, properties and the initial set of templates, respectively. User-defined reference data specific to companies or organizations are defined by inheriting from the initial reference data and the initial set of templates. In order to support the extension of reference data and templates, an editor that provides creation, deletion and modification functions of user-defined reference data is needed. In this study, an editor for reference data based on ISO 15926 was developed. Sample reference data were encoded in OWL (web ontology language) according to the specification of ISO 15926 Part 8. iRINGTools and dot15926Editor were benchmarked for the design of GUI (graphical user interface). Reference data search, creation, modification, and deletion functions were implemented with XML (extensible markup language) DOM (document object model), and SPARQL (SPARQL protocol and RDF query language).

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1174-1182
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• 2017

The research and development on Web of Data to realize the opening and sharing of diverse, heterogonous data on the Web has been actively accomplished. As a standard data model for this effort, LOD (Linked Open Data) based on ontology has been proposed. A specialized instance generation system is vital to the development of LOD-based system effectively. This paper implements i-Manager as an appropriate environment for the development of LOD instances, considering the requirements of LOD systems and the practical development environment of the diverse application domains. i-Manager separates the instance layer from the ontology layer by way of LOD Interface Sheet (LIS) and implements the specialized functions requested in LOD instance development, such as instance edit/store, visualization and SPARQL query processing. This paper proposes a new approach for LOD instance development, and i-Manager can be applied for the practical LOD development environment in the diverse application areas.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.21-27
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• 2017

To guarantee the effectiveness of the HACCP(Hazard analysis and critical control points) system, it is necessary to develop of an ontology-based information system that can automatically manage the large amount of HACCP records or information derived from the HACCP operation results. In this paper, we construct a food information ontology which represents the relationships between ingredients, recipe, and features of food categories. Moreover, we develop HACCP automation application adopt the ontology to verify the semantic quality of the designed ontology model by performing HACCP processes such as HACCP diet classification. We expect to contribute to develop a food hygiene information and improve the accuracy of the HACCP data through the semantic system.