• Journal of The Korean Association For Science Education
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• v.39 no.5
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• pp.655-668
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• 2019

In this study, we investigated the middle school students' processes of scientific graph construction from the perspective of the problem solving process. Ten 9th graders participated in this study. They constructed a scientific graph based on pictorial data depicting precipitation reaction. The think-aloud method was used in order to investigate their thinking processes deeply. Their activities were videotaped, and semi-structured interviews were also conducted. The analysis of the results revealed that their processes of scientific graph construction could be classified into four types according to the problem solving strategy and the level of representations utilized. Students using the structural strategy succeeded in constructing scientific graph regardless of the level of representation utilized, by analyzing the data and identifying the trend based on the propositional knowledge about the target concept of the graph. Students of random strategy-higher order representation type were able to succeed in constructing scientific graph by systematically analyzing the characteristics of the data using various representations, and considering the meaning of the graph constructed in terms of the scientific context. On the other hand, students of random strategy-lower order representation type failed to construct correct scientific graph by constructing graph in a way of simply connecting points, and checking the processes of graph construction only without considering the scientific context. On the bases of the results, effective methods for improving students' ability to construct scientific graphs are discussed.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.301-304
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• 2002

Recently, database technologies have been used commonly. But, ordinary technologies aren't suitable to construct a complicated database such as a classical literature database or an archaeological relic's database. Because this kinds of data are semi-structured data that doesn't have regular structures, database schema can't be defined before databases. We have proposed DREAM model for semi-structured databases. In this model, a database consists of five elements and the model has operations similar to operation of set theory. And further we have introduced dynamic schema "shape" showing structure of each element. We have already realized a prototype of DBMS adopting DREAM model (DREAM DBMS) and constructing function of shapes. However, shape is imperfect to describe database structures because it can't explain nested structures of elements. In this paper, we will profuse a "shape graph"that is dynamic schema showing database structures more exactly and extend the DREAM DBMS. Further we will evaluate the performance of constructing function of shapes and shape graphs.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.163-180
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• 2022

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show difficulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only vertically extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulness. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we collected real estate questions used in real life and selected 9 different questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. According to this result, we have found out that a Graph Database such as Neo4j is more efficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.

• Journal of Integrative Natural Science
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• v.2 no.4
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• pp.243-251
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• 2009

This paper surveys some researches to accomplish on bioinformatics. These researches wish to propose a database architecture combining a general view of bioinformatics data as a graph of data objects and data relationships, with the efficiency and robustness of data management and query provided by indexing and generic programming techniques. Here, these invert the role of the index, and make it a first-class citizen in the query language. It is possible to do this in a structured way, allowing users to mention indexes explicitly without yielding to a procedural query model, by converting functional relations into explicit functions. In the limit, the database becomes a graph, in which the edges are these indexes. Function composition can be specified either explicitly or implicitly as path queries. The net effect of the inversion is to convert the database into a hyperdatabase: a database of databases, connected by indexes or functions. The inversion approach was motivated by their work in biological databases, for which hyperdatabases are a good model. The need for a good model has slowed progress in bioinformatics.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1120-1128
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• 1999

The model-based object recognition is defined as a graph matching process between model images and an input image. In this paper, a graph matching problem is modeled as a n optimization problems and a genetic algorithm is proposed to solve the problems. For this work, fitness function, data structured and genetic operators are developed The simulation results are shown that the proposed genetic algorithm can match feature points between model image and input image for recognition of partially occluded two-dimensional objects. The performance fo the proposed technique is compare with that of a neural network technique.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.277-288
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• 2021

Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.411-421
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• 2002

In this Paper we propose a new Image analysis algorithm for microarray processing and a method to locate the position of the grid cell using the topology of the grid spots. Microarray is a device which enables a parallel experiment of 10 to 100 thousands of test genes in order to measure the gene expression. Because of the huge data obtained by a experiment automated image analysis is needed. The final output of this microarray experiment is a set of 16-bit gray level image files which consist of grid-structured spots. In this paper we propose one algorithm which located the address of spots (spot indices) using graph structure from image data and a method which determines the precise location and shape of each spot by measuring the inclination of grid structure. Several experiments are given from real data sets.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.9-21
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• 2020

With the proliferation of RDF knowledge graphs(KGs), there arose a need of a standardized schema representation of the graph model for effective data interchangeability and interoperability. The need resulted in the development of SHACL specification to describe and validate RDF graph's structure by W3C. Relational databases(RDBs) are one of major sources for acquiring structured knowledge. The standard for automatic generation of RDF KGs from RDBs is R2RML, which is also developed by W3C. Since R2RML is designed to generate only RDF data graphs from RDBs, additional manual tasks are required to create the schemas for the graphs. In this paper we propose an approach to automatically generate SHACL schemas for RDF KGs populated by R2RML mappings. The key of our approach is that the SHACL shemas are built only from R2RML documents. We describe an implementation of our appraoch. Then, we show the validity of our approach with R2RML test cases designed by W3C.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2643-2657
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• 2023

Scene graphs are structured representations that can clearly convey objects and the relationships between them, but are often heavily biased due to the highly skewed, long-tailed relational labeling in the dataset. Indeed, the visual world itself and its descriptions are biased. Therefore, Unbiased Scene Graph Generation (USGG) prefers to train models to eliminate long-tail effects as much as possible, rather than altering the dataset directly. To this end, we propose Geometric and Semantic Improvement (GSI) for USGG to mitigate this issue. First, to fully exploit the feature information in the images, geometric dimension and semantic dimension enhancement modules are designed. The geometric module is designed from the perspective that the position information between neighboring object pairs will affect each other, which can improve the recall rate of the overall relationship in the dataset. The semantic module further processes the embedded word vector, which can enhance the acquisition of semantic information. Then, to improve the recall rate of the tail data, the Class Balanced Seesaw Loss (CBSLoss) is designed for the tail data. The recall rate of the prediction is improved by penalizing the body or tail relations that are judged incorrectly in the dataset. The experimental findings demonstrate that the GSI method performs better than mainstream models in terms of the mean Recall@K (mR@K) metric in three tasks. The long-tailed imbalance in the Visual Genome 150 (VG150) dataset is addressed better using the GSI method than by most of the existing methods.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.309-320
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• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.