• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.290-293
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• 2006

The goal of research is to propose a framework for drawing data search system which is based on the web. The existing search systems were reviewed in the form of case studies and thereby the limitation were addressed: the unsystematic translation between the presentational building model and the discursive design criteria. besides the limited area in sharing and space. Therefore, a web-based drawing search with common structure which building representational model and building behavior model is proposed. The system contains a number of phases: firstly, a user is required to build a building model with the proposed building representational model and then this model is automatically transformed into an aspect model; secondly, a user is also required to present his query in form of the propose building behavior model by web page; finally, these two models - building representational model and building behavior model - are compared by database data so as to retrieve the proper result.

• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.823-834
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• 2001

In the active databases, whenever an event occurs, active rules with the matching event specifications are triggered automatically, its action will be executed. Because these rules may in turn trigger other rules including themselves, the set of rules may be executing each other indefinitely. These problem can be solved by rule termination analysis, and it is efficient for the rule termination to execute in compile time of rule. In this paper we not only design rule compiler with rule termination analyzer, but also propose its execution model and algorithm. The completeness of proposed model is verified by algorithm formalization of rule termination analysis.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.879-890
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• 2000

Designing a multilevel database application is a complex process, and the entities and their associated security levels must be represented using an appropriate model unambiguously. It is also important to capture the semantics of a multilevel databse application as accurate and complete as possible. Owing to the focus of the IDEA Methodology for designing the non-secure database applications on the data-intensive systems, the Object Model describes the static structure of the objects in an application and their relationships. That is, the Object Model in the IDEA Methodology is an extended Entity-Relationship model giving a static description of objects. The IDEA Methodology has not been developed the multilevel secure database applications, but by using an existing methodology we could take advantage of the various techniques that have already been developed for that methodology. That is, this way is easier to design the multilevel secure schema than to develop a new model from scratch. This paper adds the security features 새？ Object Model in the IDEA Methodology, and presents the transformation from this model to a multilevel secure object oriented schema. This schema will be the preliminary work which can be the general scheme for the automatic mapping to the various commercial multilevel secure database management system such as Informix-Online/Secure, Trusted ORACLE, and Sybase Secure SQL Server.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.102-116
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• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.165-178
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• 2005

A product classification scheme is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eCl@ss, however, have a lot of limitations to meet these requirements for dynamic features of classification. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this Paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes, and describe the semantic classification model proposed in [1], which satisfies the requirements for dynamic features of product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.435-442
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• 2016

Obesity extends the global economic burden and it causes that the failure of a reduction of physical activity, and diet management. In this work, nutritional information and personalized diet based on calorie supply system and is discriminatory utilized the obesity-related SNP information in order to recommend a personalized functional foods. This study performed a GWAS analysis for the excavation of a Korean-specific and obesity-related SNP, which utilizes genetic information were recommended by entering a personalized diet in accordance with the SNP genotype-phenotype information. In addition, we integrated Database with relation of nutrient for utilizing the USDA Food information and it was applied to recommend with Sickdanome. As a result, the obesity-related SNP information was confirmed in the sample which has the normal value BMI. In this study, we have recognized that the phenotype information related obesity, BMI is inconsistent with the SNP genotype information. This result is shown that it is necessary to provide the personalized dietary SickdanOme recommendation based on the both pheotype-genotype information.

• 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 the Korean Society for information Management
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• v.26 no.3
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• pp.261-278
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• 2009

Identifying optimal feature sets in Text Categorization(TC) is crucial in terms of improving the effectiveness. In this study, experiments on feature expansion were conducted using author provided keyword sets and article titles from typical scientific journal articles. The tool used for expanding feature sets is WordNet, a lexical database for English words. Given a data set and a lexical tool, this study presented that feature expansion with synonymous relationship was significantly effective on improving the results of TC. The experiment results pointed out that when expanding feature sets with synonyms using on classifier names, the effectiveness of TC was considerably improved regardless of word sense disambiguation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2256-2262
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• 2010

The requirements for automated handing of business process and its monitoring usually have a proprietary form for each enterprise. Unlike the conventional database transaction, business process takes long time for its completion and incorporates very complex handling logics along with business situations. Since those handling logics are frequently changing in accordance with the business policies or environment, enterprises want to integrally capture the whole business semantics while monitoring those process instances. In this paper, we adopted GMA(Grid Monitoring Architecture) for the integrated monitoring of business processes. The GMA(Grid Monitoring Architecture) is a very scalable architecture to effectively monitor and manage monitoring information under the heterogeneous environment. By introducing complex event handling features into GMA to support various processing logics, we could implement a system that enables automated execution and high-level monitoring of business processes.