• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.374-376
• /
• 2023

최근 환율 예측에 관한 다양한 연구가 진행되어 왔다. 이러한 추세에 대응하여 본 연구에서는 Pearson 상관 계수 및 상호 정보를 사용하여 외환 시장의 환율 변동을 분석하는 다중 연결 네트워크를 구축하였다. 본 연구에서는 이러한 구성된 환율 변화에 대한 시공간 의존성 네트워크를 만들고 그래프 기계 학습의 잠재력을 조사하여 예측 정확도를 향상시키려고 노력하였다. 본 연구 결과는 선형 및 비선형 종속 네트워크 모두에 대해 그래프 신경망을 활용한 임베딩을 활용하여 기존의 기계 학습 알고리즘과 결합시킬 경우 환율 변화의 예측력이 향상될 수 있음을 경험적으로 확인하였다. 특히, 이러한 결과는 통화 간 상호 의존성에만 의존하여 추가 데이터 없이 달성되었다. 이 접근 방식은 데이터 효율성을 강화하고 그래프 시각화를 통해 설명력 있는 통찰력을 제공하며 주어진 데이터 세트 내에서 효과적인 데이터를 생성하여 예측력을 높이는 결과로 해석할 수 있다.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.10
• /
• pp.23-34
• /
• 2020

In this paper, we proposes a method to automatically construct SHACL schemas for RDF knowledge graphs(KGs) generated by Direct Mapping(DM). DM and SHACL are all W3C recommendations. DM consists of rules to transform the data in an RDB into an RDF graph. SHACL is a language to describe and validate the structure of RDF graphs. The proposed method automatically translates the integrity constraints as well as the structure information in an RDB schema into SHACL. Thus, our SHACL schemas are able to check integrity instead of RDBMSs. This is a consideration to assure database consistency even when RDBs are served as virtual RDF KGs. We tested our results on 24 DM test cases, published by W3C. It was shown that they are effective in describing and validating RDF KGs.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.5
• /
• pp.892-898
• /
• 2012

Rough classification methods are used to improving the recognition speed in many character recognition problems. In this case, some irreversible result can occur by an error in rough classification. Methods for duplicating each model in several classes are used in order to reduce this risk. But the errors by rough classfication can not be completely ruled out by these methods. In this paper, an recognition method is proposed to increase speed that matches models selectively without any increase in error. This method constructs a model graph using similarity between models. Then a search process begins from a particular point in the model graph. In this process, matching of unnecessary models are reduced that are not similar to the input pattern. In this paper, the proposed method is applied to the recognition problem of handwriting numbers and upper/lower cases of English alphabets. In the experiments, the proposed method was compared with the basic method that matches all models with input pattern. As a result, the same recognition rate, which has shown as the basic method, was obtained by controlling the out-degree of the model graph and the number of maintaining candidates during the search process thereby being increased the recognition speed to 2.45 times.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.7
• /
• pp.1805-1816
• /
• 1999

This paper presents the Module Class Dependency Graph for expressing the dependency relations between classes effectively. The object-oriented language is developed independently at design time, and consists of relationship between classes. Therefore we need to consider these characteristics of independence, and to express effectively the relation of classes which is existed in class hierarchy. In the System Dependence Graph and Class Dependence Graph, the relationship of classes is not expressed. To express the class relationship, we propose the Module Class Dependence Graph, and we verify the effectiveness of this method applying to object constructor, inheritance relationship and dynamic binding. Also, we presents the expressing method of parameter to identify the member data of classes. Using this Module Class Dependency Graph, we can analyze the relationship of module class correctly at design time. This method can be applied to reverse engineering, testing, visualization and other various fields to analyze system.

• The Journal of the Korea Contents Association
• /
• v.12 no.8
• /
• pp.1-12
• /
• 2012

We develop the Spatio-Temporal Graph to imbue the historical context to the situations in a virtual world, and an ontology to enable a structural description of their elements such as the objects, relationships, and activities. In the time dimension the graph models all the temporal phases of the future besides the past and present in a comprehensive manner, and all the spatial aspects in an intuitive but efficient fashion. The overall architecture composing the Physical Layer, Logical Layer and Conceptual Layer which are integrated according to their interrelations allows events occurring in their corresponding worlds to be simulated in historical context. The S-T Graph could be used both to simulate the situations in the virtual world and to realize the knowledge systems of the virtual inhabitants to be used in judging and evaluating those situations. By adding temporal changes to the multi-layered architecture of our virtual world, this model lays a foundation for maximizing the diversity of situations in the simulation of a virtual world.

• Journal of Intelligence and Information Systems
• /
• v.12 no.3
• /
• pp.95-110
• /
• 2006

To enhance the rule-based reasoning capability of Semantic Web, the XRML (eXtensible Rule Markup Language) approach embraces the meta-information necessary for the extraction of explicit rules from Web pages and its maintenance. To effectuate the automatic identification of rules from unstructured texts, this research develops a framework of using rule ontology. The ontology can be acquired from a similar site first, and then can be used for multiple sites in the same domain. The procedure of ontology-based rule identification is regarded as a graph search problem with incomplete nodes, and an A* algorithm is devised to solve the problem. The procedure is demonstrated with the domain of shipping rates and return policy comparison portal, which needs rule based reasoning capability to answer the customer's inquiries. An example ontology is created from Amazon.com, and is applied to the many online retailers in the same domain. The experimental result shows a high performance of this approach.

• Journal of The Korean Association For Science Education
• /
• v.22 no.4
• /
• pp.768-778
• /
• 2002

Science teachers often suppose that students are able to know the symbolical meaning of graphs when they see the graphs. But such a assumption is not based on the firm theories but a mere image. And we need to search them for holding the abilities to construct and to interpret. In addition, unfortunately, many researchers show that they scarcely have the graphing skills. And then, The Test of Graphing in Science(TOGS) was administered to 535 7th to 12th graders, for we search them for holding the graphing abilities to some degree. Though the higher grade, the better score, they lack the first three among 9 objectives of TOGS which are scaling axes, assigning variables to the axes, using a best fit line, plotting points, translating a graph that displays the data, selecting the corresponding value for y(or x), interrelating/extrapolating graphs, describing the relationship between variables, interrelating the results of the two graphs. It was concluded from this that subjects' graph construction is lower than their graph interpretation in graph skills. It suggests that school science have a bias toward graph interpretation. This tendency represents more strikingly in the case of upper students in TOGS than the others'.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.77-88
• /
• 2020

Recently, many researches habe been done to organize and analyze various complex relationships in real world, represented in the form of graphs. In particular, the computer field literature data system, such as DBLP, is a representative graph data in which can be composed of papers, their authors, and citation among papers. Becasue graph data is very complex in storage structure and expression, it is very difficult task to search, analysis, and visualize a large size of bibliographic big data. In this paper, we develop a graphic user interface tool, called EEUM, which visualizes bibliographic big data in the form of graphs. EEUM provides the features to browse bibliographic big data according to the connected graph structure by visually displaying graph data, and implements search, management and analysis of the bibliographc big data. It also shows that EEUM can be conveniently used to search, explore, and analyze by applying EEUM to the bibliographic graph big data provided by DBLP. Through EEUM, you can easily find influential authors or papers in every research fields, and conveniently use it as a search and analysis tool for complex bibliographc big data, such as giving you a glimpse of all the relationships between several authors and papers.

• The KIPS Transactions:PartB
• /
• v.19B no.3
• /
• pp.189-194
• /
• 2012

In this paper, we propose a query expansion method based on word graphs using pseudo-relevant and pseudo non-relevant documents to achieve performance improvement in information retrieval. The initially retrieved documents are classified into a core cluster when a document includes core query terms extracted by query term combinations and the degree of query term proximity. Otherwise, documents are classified into a non-core cluster. The documents that belong to a core query cluster can be seen as pseudo-relevant documents, and the documents that belong to a non-core cluster can be seen as pseudo non-relevant documents. Each cluster is represented as a graph which has nodes and edges. Each node represents a term and each edge represents proximity between the term and a query term. The term weight is calculated by subtracting the term weight in the non-core cluster graph from the term weight in the core cluster graph. It means that a term with a high weight in a non-core cluster graph should not be considered as an expanded term. Expansion terms are selected according to the term weights. Experimental results on TREC WT10g test collection show that the proposed method achieves 9.4% improvement over the language model in mean average precision.

• Journal of The Korean Association For Science Education
• /
• v.43 no.3
• /
• pp.237-251
• /
• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.