• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.65-75
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• 2008

Grid Computing has enabled enormous amount of computational jobs by connecting distributed computing resources. This technology has developed and widely used in various fields. Previous researches usually focused on how to efficiently manage and use the grid resources. However, there was not enough tries to understand and manage information of application softwares in a well-defined structure. Therefore users in application domain need to how about grid deeply to identify and describe the resource requirements matching for each jobs. We introduce a semantic grid management system based on application ontology to overcome this problem. We design and implement the ontology to store various information of the applications. With the ontology, this system can infer the resource requirements from input parameters and input data of the application software and automatically assign appropriate resources by matching the requirement. Also it can transform the information to other forms which grid middlewares can handle. We apply the system to construct an analysis environment of bioinformatics and compare it with other grid systems to explain usefulness of the system.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.177-185
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• 2006

It is a difficult problem that learner have to need much times and efforts to search informations for problem solving. This is caused that the web based search system used by this time have the searching method of simple keyword matching. The searching method of simple keyword matching search informations by method of whether it is simply matched with keyword. Therefore, Learner have to much times and efforts to search informations, and may lose or be out of his bearing. To solve this problems, We design and implement a ontology based search system. This system is apply to PBL of social studies on middle school students. As a result, This system is more effect than the web based search system used by this time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.181-186
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• 2005

In collaborative environment, it is necessary that the participants in collaboration should share the same understanding about the semantics of terms. For example, they should know that 'Part' and 'Item' are different word-expressions for the same meaning. In this paper, we consider sharing between CAD and PDM data. In order to handle such problems in information sharing, an information system needs to automatically recognize that the terms have the same semantics. Serving this purpose, the semantic mapping logic and the ontology based mapper system is described in this paper. In the semantic mapping logic topic, we introduce our logic that consists of four modules: Character Matching, Instance Reasoning, definition comparing and Similarity Checking. In the ontology based mapper, we introduce the system architecture and the mapping procedure.

• Korean Journal of Computational Design and Engineering
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• v.11 no.3
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• pp.211-222
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• 2006

In collaborative environment, it is necessary that the participants in collaboration should share the same understanding about the semantics of terms. For example, they should know that 'COMPONENT' and 'ITEM' are different word-expressions for the same meaning. In order to handle such problems in information sharing, an information system needs to automatically recognize that the terms have the same semantics. So we develop an algorithm mapping two terms based on their ontological definitions and their similarities. The proposed algorithm consists of four steps: the character matching, the inferencing, the definition comparing and the similarity checking. In the similarity checking step, we consider relation similarity and hierarchical similarity. The algorithm is very primitive, but it shows the possibility of semi-automatic mapping using ontology. In addition, we design a mapping procedure for a mapping system, called SOM (semantic ontology mapper).

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.229-236
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• 2005

The Semantic Web enables machines to achieve an effective retrieval, integration, and reuse of web resources. The keyword search method currently used has a limit to accurate search results because of a simple string matching method in web environment. This paper proposes an Ontology-Based Information Retrieval which can solve the problems and retrieve better search results through semantic relations. In this system, we implemented the Cultural Assets Ontology based on OWL with RDQL and Jena API. we also suggest a method to handle properties stored in a database.

• Annual Conference of KIPS
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• 2013.05a
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• pp.470-471
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• 2013

Accuracy of mappings is the key for achieving true interoperability among different healthcare systems. The initial step towards interoperable healthcare systems is compliancy with healthcare standards (HL7, openEHR, CEN 13606). Ontologies for these standards are developed that require ontology matching to generate generalized ontology mappings. Organizations conform to specific concepts of different standards based on their requirements. This step is called as conformance claims and is based on Personalized-Detailed Clinical Model. It invalidates some of the generalized mappings because of non-conformed concepts and leads to the necessity of the proposed technique of customized ontology mappings. These customized ontology mappings compliment the generalized ontology mapping to increase the level of accuracy of mappings and thus achieving data interoperability. The proposed system ensures quality of care to patients by timely delivery of healthcare information.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.213-220
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• 2011

In the current construction industry where various stakeholders take part, BIM Data exchange using standard format can provide a more efficient working environment for related staffs during the life-cycle of the building. Currently, the formats used to exchange the data from 3D-CAD application to structure energy analysis at the design stages are IFC, the international standard format provided by IAI, and gbXML, developed by Autodesk. However, because of insufficient data compatibility, the BIM data produced in the 3D-CAD application cannot be directly used in the energy analysis, thus there needs to be additional data entry. The reasons for this are as follows: First, an IFC file cannot contain all the data required for energy simulation. Second, architects sometimes write material names on the drawings that are not matching to those in the standard material library used in energy analysis tools. DOE-2.2 and Energy Plus are the most popular energy analysis engines. And both engines have their own material libraries. However, our investigation revealed that the two libraries are not compatible. First, the types and unit of properties were different. Second, material names used in the library and the codes of the materials were different. Furthermore, there is no material library in Korean language. Thus, by comparing the basic library of DOE-2, the most commonly used energy analysis engine worldwide, and EnergyPlus regarding construction materials; this study will analyze the material data required for energy analysis and propose a way to effectively enter these using semantic web's ontology. This study is meaningful as it enhances the objective credibility of the analysis result when analyzing the energy, and as a conceptual study on the usage of ontology in the construction industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of Information Science Theory and Practice
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• v.12 no.3
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• pp.49-62
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• 2024

Strategy monitoring is essential for business management and for administrators, including managers and executives, to build a data-driven organization. Having a tool that is able to visualize strategic data is significant for business intelligence. Unfortunately, there are gaps between business users and information technology departments or business intelligence experts that need to be filled to meet user requirements. For example, business users want to be self-reliant when using business intelligence systems, but they are too inexperienced to deal with the technical difficulties of the business intelligence systems. This research aims to create an automatic matching framework between the key performance indicators (KPI) formula and the data in database systems, based on ontology concepts, in the case study of Prince of Songkla University. The mapping data schema with ontology (MapDSOnto) framework is created through knowledge adaptation from the literature review and is evaluated using sample data from the case study. String similarity methods are compared to find the best fit for this framework. The research results reveal that the "fuzz.token_set_ratio" method is suitable for this study, with a 91.50 similarity score. The two main algorithms, database schema mapping and domain schema mapping, present the process of the MapDS-Onto framework using the "fuzz.token_set_ratio" method and database structure ontology to match the correct data of each factor in the KPI formula. The MapDS-Onto framework contributes to increasing self-reliance by reducing the amount of database knowledge that business users need to use semantic business intelligence.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.329-334
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• 2009

The ontology alignment has two kinds of major problems. First, the features used for ontology alignment are usually defined by experts, but it is highly possible for some critical features to be excluded from the feature set. Second, the semantic and the structural similarities are usually computed independently, and then they are combined in an ad-hoc way where the weights are determined heuristically. This paper proposes the modified parse tree kernel (MPTK) for ontology alignment. In order to compute the similarity between entities in the ontologies, a tree is adopted as a representation of an ontology. After transforming an ontology into a set of trees, their similarity is computed using MPTK without explicit enumeration of features. In computing the similarity between trees, the approximate string matching is adopted to naturally reflect not only the structural information but also the semantic information. According to a series of experiments with a standard data set, the kernel method outperforms other structural similarities such as GMO. In addition, the proposed method shows the state-of-the-art performance in the ontology alignment.