• 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 Korean Port Research
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• v.12 no.1
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• pp.55-64
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• 1998

Directed graphic algorithm was applied to an empirical analysis of traffic occupant fatalities based on a model by Crandall. In this paper, Crandall's data on U.S. traffic fatalities for the period 1947-1981 are focused and extended to include 1982-1993. Based on the 1947-1981 annual data, the directed graph algorithms reveal that occupant traffic deaths are directly caused by income, vehicle miles, and safety devices. Vehicle mileage is caused by income and rural driving. The estimation is conducted using three stage least squares regression. Those results show a difference between the traditional regression methodology and causal graphical analysis. It is also found that forecasts from the directed graph based model outperform forecasts from the regression-based models, in terms of mean squared forecasts error. Furthermore, it is demonstrates that there exists some latent variables between all explanatory variables and occupant deaths.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.335-342
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• 2015

The increasing interests on patents have led many individuals and companies to apply for many patents in various areas. Applied patents are stored in the forms of electronic documents. The search and categorization for these documents are issues of major fields in data mining. Especially, the keyword extraction by which we retrieve the representative keywords is important. Most of techniques for it is based on vector space model. But this model is simply based on frequency of terms in documents, gives them weights based on their frequency and selects the keywords according to the order of weights. However, this model has the limit that it cannot reflect the relations between keywords. This paper proposes the advanced way to extract the more representative keywords by overcoming this limit. In this way, the proposed model firstly prepares the candidate set using the vector model, then makes the graph which represents the relation in the pair of candidate keywords in the set and selects the keywords based on this relationship graph.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.84-94
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• 2021

Graph databases have been developed to efficiently store and query graph data composed of vertices and edges to express relationships between objects. Since the query types of graph database show very different characteristics from traditional NoSQL databases, benchmarking tools suitable for graph databases to verify the performance of the graph database are needed. In this paper, we propose an efficient graph database benchmarking system that supports diversity in graph inputs and queries. The proposed system utilizes OrientDB to conduct benchmarking for graph databases. In order to support the diversity of input graphs and query graphs, we use LDBC that is an existing graph data generation tool. We demonstrate the feasibility and effectiveness of the proposed scheme through analysis of benchmarking results. As a result of performance evaluation, it has been shown that the proposed system can generate customizable synthetic graph data, and benchmarking can be performed based on the generated graph data.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.243-250
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• 2020

Knowledge graph-based question answering not only requires deep understanding of the given natural language questions, but it also needs effective reasoning to find the correct answers on a large knowledge graph. In this paper, we propose a deep neural network model for effective reasoning on a knowledge graph, which can find correct answers to complex questions requiring multi-hop inference. The proposed model makes use of highly expressive bilinear graph neural network (BGNN), which can utilize context information between a pair of neighboring nodes, as well as allows bidirectional feature propagation between each entity node and one of its neighboring nodes on a knowledge graph. Performing experiments with an open-domain knowledge base (Freebase) and two natural-language question answering benchmark datasets(WebQuestionsSP and MetaQA), we demonstrate the effectiveness and performance of the proposed model.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.35-46
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• 2022

Recently, with the development of network technologies, as IoT and social network service applications have been actively used, a lot of graph stream data is being generated. In this paper, we propose a graph compression scheme that considers the stream graph environment by applying graph mining to the existing compression technique, which has been focused on compression rate and runtime. In this paper, we proposed Incremental frequent pattern based compression technique for graph streams. Since the proposed scheme keeps only the latest reference patterns, it increases the storage utilization and improves the query processing time. In order to show the superiority of the proposed scheme, various performance evaluations are performed in terms of compression rate and processing time compared to the existing method. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1414-1421
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• 1997

This paper examines the physics and mechanics governing the dynamic interaction between physical systems and suggests the four structures of bond graph prototypes, considered as a general model that can promise their dynamic behavior physically resonable. The bond graph prototypes originating from the paper are more realistic junction structures than those used to model dynamic systems conventionally by bond graph standards in whether physical constraints are involved or not when the energy exchange between two dynamic components arises. It is shown that the bond graph prototypes are dynamic or energetic in their describing equations compared to the bond graph standards, and connectivity and causality are properties of dynamic systems upon which the steps developed in this paper for the bond graph prototypes are wholly based and their definitions an concepts are highly emphasized all through the paper.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.156-164
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• 2017

Measuring the similarity of two graphs is a basic tool to solve graph problems in various applications. Most graph algorithms have a high time complexity according to the number of vertices and edges. Because Graphics Processing Units (GPUs) have a high computational power and can be obtained at a low cost, these have been widely used in graph applications to improve execution time. This paper proposes an efficient parallel algorithm to measure graph similarity using the CUDA on a GPU environment. The experimental results show that the proposed approach brings a considerable improvement in performance and efficiency when compared to CPU-based results. Our results also show that the performance is improved significantly as the size of the graph increases.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.27-37
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• 2021

Highly informative heuristics in AI planning can help to a more efficient search a solutions. However, in general, to obtain informative heuristics from planning problem specifications requires a lot of computational effort. To address this problem, we propose a Partial Planning Graph(PPG) and Mixed Heuristics for solving planning problems more efficiently. The PPG is an improved graph to be applied to can find a partial heuristic value for each goal condition from the relaxed planning graph which is a means to get heuristics to solve planning problems. Mixed Heuristics using PPG requires size of each graph is relatively small and less computational effort as a partial plan generated for each goal condition compared to the existing planning graph. Mixed Heuristics using PPG can find partial interactions for each goal conditions in an effective way, then consider them in order to estimate the goal state heuristics. Therefore Mixed Heuristics can not only find interactions for each goal conditions more less computational effort, but also have high accuracy of heuristics than the existing max and additive heuristics. In this paper, we present the PPG and the algorithm for computing Mixed Heuristics, and then explain analysis to accuracy and the efficiency of the Mixed Heuristics.