• Magazine of the Korean Society of Agricultural Engineers
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• v.28 no.1
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• pp.33-40
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• 1986

Unit-graph model to estimate the daily streamfiow was developed on the basis of distribution graph method. The results of evaluating the application of the model to Nakdong watersheds were generally satisfactory and this model would be the groundwork of the "Unit-graph model for daily streamflow in Korean watersheds".eds".uot;.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.864-867
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• 1995

Presented in the paper is a structured approach to modeling automated manufacturing system (AMS) in the form of an event graph. The proposed two-phase procedure for formal modeling is 1) reference modeling by schematic supervisory control modeling and 2) event graph transformation from supervisory control model. Also described is a formal model for a small-sized FMS in the form of an event graph.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.993-1001
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• 2003

A RDB Storage Model based on the Edge-Labeled Graph is suggested for store the XML instance in Relational Databases(RDB). The XML instance being stored is represented by Data Graph based on the Edge-Labeled Graph. Data Path Table, Element, Attribute, and Table Index Table values are extracted. Then Database Schema is defined, and the extracted values are stored using the Mapper. In order to support querry, Repository Model offers the translator translating XQL which is used as query language under XPATH, into SQL. In addition, it creates DBtoXML generator restoring the stored XML instance. As a result, storage relationship between the XML instance and proposed model structure can be expressed in terms of Graph-based Path, and it shows the possibility of easy search of random Element and Attribute information.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.103-113
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• 2014

This study designed and implemented a query model for consecutive analyses of dynamic multivariate graph data. First, the query model consists of two procedures; setting the discriminant function, and determining an alteration method. Second, the query model was implemented as a query system that consists of a query panel, a graph visualization panel, and a property panel. A Node-Link Diagram and the Force-Directed Graph Drawing algorithm were used for the visualization of the graph. The results of the queries are visually presented through the graph visualization panel. Finally, this study used the data of worldwide import & export data of small arms to verify our model. The significance of this research is in the fact that, through the model which is able to conduct consecutive analyses on dynamic graph data, it helps overcome the limitations of previous models which can only perform discrete analysis on dynamic data. This research is expected to contribute to future studies such as online decision making and complex network analysis, that use dynamic graph models.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.800-807
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• 2022

This study aims to review the use of graph databases in construction research. Based on the diagnosis of the current research status, a future research direction is proposed. The use of graph databases in construction research has been increasing because of the efficiency in expressing complex relations between entities in construction big data. However, no study has been conducted to review systematically the status quo of graph databases. This study analyzes 42 papers in total that deployed a graph model and graph database in construction research, both quantitatively and qualitatively. A keyword analysis, topic modeling, and qualitative content analysis were conducted. The review identified the research topics, types of data sources that compose a graph, and the graph database application methods and algorithms. Although the current research is still in a nascent stage, the graph database research has great potential to develop into an advanced stage, fused with artificial intelligence (AI) in the future, based on the active usage trends this study revealed.

• The Journal of Information Systems
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• v.5
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• pp.149-173
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• 1996

This paper focus on the development of object-oriented model bases for Structured Modeling. For the model base organization, object modeling techniques and model typing concept which is similar to data typing concept are used. Structured modeling formalizes the notion of a definitional system as a way of dscribing models. From the object-oriented concept, a structured model can be represented as follows. Each group of similar elements(genus) is represented by a composite class. Other type of genera can be represented in a similar manner. This hierarchical class composition gives rise to an acyclic class-composition graph which corresponds with the genus graph of structured model. Nodes in this graph are instantiated to represent the elemental graph for a specific model. Taking this class composition process one step further, we aggregate the classes into higher-level composite classes which would correspond to the structured modeling notion of a module. Finally, the model itself is then represented by a composite class having attributes each of whose domain is a composite class representing one of the modules. The resulting class-composition graph represent the modular tree of the structured.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.33-42
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• 2003

A XML Instance repository model based on the Edge-Labeled Graph is suggested for storing the XML instance in Relational Databases, This repository model represents the XML instance as a data graph based on the Edge-Labeled Graph, extracts the defined value based on the structure of data path, element, attribute, and table index table presented as database schema, and stores these values using the Mapper module, In order to support querry, XML repository model offers the module translating XQL which is a query language under XPATH to SQL, and has DBtoXML generator module restoring the stored XML instance. As a result, it is possible to represent the storage relationship between the XML instances and the proposed repository model in terms of Graph-based Path, and it shows the possibility of easy search of specific element and attribute information.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.425-430
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• 2002

An automatic transmission model using the bond graph techniques is developed for analyzing shift characteristics of vehicles. Bond graph models can be systemically manipulated to yield state space equations of standard form. Bond graph techniques are applied for modeling overall automatic transmission systems and shift models. A fuzzy controller is synthesized for the verification of a shifting model in the ${1^st} gear to the {2^nd}$ gear. Simulation results show the fitness of models by the bond graph techniques.