• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.20-23
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• 2020

개체 연결이란 문서에서 등장한 멘션(Mention)들을 지식 기반(Knowledge Base)상의 하나의 개체에 연결하는 문제를 말한다. 개체 연결은 개체를 찾는 멘션 탐지(mention detection)과정과 인식된 멘션에 대해 중의성을 해결하여 하나의 개체를 찾는 개체 중의성 해결(Entity disambiguation)과정으로 구성된다. 본 논문에서는 개체 정보를 강화하기 위해 wikipedia2vec정보를 결합하여 Entity 정보를 강화하고 문장 내에 모든 개체 정보를 활용하기 위해 집합적 개체를 정의하고 그래프 구조를 표현하기 위해 GNN을 활용하여 기존보다 높은 성능을 이끌어내었다.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.582-587
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• 2021

한국어 통합 지식베이스를 생성하기 위해 필수적인 분류체계(taxonomy)를 구축하는 방식을 제안한다. 위키데이터를 기반으로 분류 후보군을 추출하고, 상하위 관계를 통해 방향 비순환 그래프(Directed Acyclic Graph)를 구성한 뒤, 국부적 도달 중심성(local reaching centrality) 등의 정보를 활용하여 정제함으로써 246 개의 분류와 314 개의 상하위 관계를 갖는 분류체계를 생성한다. 워드넷(WordNet), 디비피디아(DBpedia) 등 기존 링크드 오픈 데이터의 분류체계 대비 깊이 있는 계층 구조를 나타내며, 다중 상위 분류를 지닐 수 있는 비트리(non-tree) 구조를 지닌다. 또한, 위키데이터 속성에 기반하여 위키데이터 정보가 있는 인스턴스(instance)에 자동으로 분류를 부여할 수 있으며, 해당 방식으로 실험한 결과 99.83%의 분류 할당 커버리지(coverage) 및 99.81%의 분류 예측 정확도(accuracy)를 나타냈다.

• Journal for History of Mathematics
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• v.17 no.3
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• pp.109-120
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• 2004

This study investigated three pre-service teachers' mathematical problem solving among hand-in-write-ups and final projects for each subject. All participants' activities and computer explorations were observed and video taped. If it was possible, an open-ended individual interview was performed before, during, and after each exploration. The method of data collection was observation, interviewing, field notes, students' written assignments, computer works, and audio and videotapes of pre- service teachers' mathematical problem solving activities. At the beginning of the mathematical problem solving activities, all participants did not have strong procedural and conceptual knowledge of the graph, making a model by using data, and general concept of a sine function, but they built strong procedural and conceptual knowledge and connected them appropriately through mathematical problem solving activities by using the computer technology.

• Journal of KIISE
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• v.42 no.8
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• pp.998-1009
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• 2015

In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.

• Journal of Elementary Mathematics Education in Korea
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• v.24 no.1
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• pp.1-30
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• 2020

This study was carried out in order to identify the error types of statistical graphs for statistical literacy education. We analyze the meaning of using graphs in statistical problem solving, and identify categories, frequencies, and contexts as the components of statistical graphs. Error types of representing categories and frequencies make statistics consumers see incorrect distributions of data by subjective point of view of statistics producers and visual illusion. Error types of providing contexts hinder the interpretation of statistical information by concealing or twisting the contexts of data. Moreover, the findings show that tasks provide standardized frame already for drawing graphs in order to avoid errors and pay attention to the process of drawing the graph rather than statistical literacy for analyzing data. We suggest some implications about statistical literacy education, ethical problems, and knowledge for teaching to be considered when teaching the statistical graph in elementary mathematics classes.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.974-983
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• 2005

In genetic algorithms with lotus-based encoding, static gene reordering is to locate the highly related genes closely together. It helps the genetic algorithms to create and preserve the schema of high-quality effectively. In this paper, we propose a static reordering framework for linear locus-based encoding. It differs from existing reorderings in that it is independent of problem-specific knowledge. It makes a complete graph where weights represent the interelationship between each pair of genes. And, it transforms the graph into a unweighted sparse graph by choosing the edges having relatively high weight. It finds a gene reordering by graph search method. Through the wide experiments about several problems, the method proposed in this paper shows significant performance improvement as compared with the genetic algorithm that does not rearrange genes.

• Journal of The Korean Association For Science Education
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• v.14 no.3
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• pp.304-311
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• 1994

Four science concepts were selected from high school science textbook to investigate the differences in priorities of students knowledge activated during solving earth science problems in laboratory and earth science environmental contexts. Two items, one for laboratory context and the other for earth environmental context, were developed for earth selected concept The subjects were constituted of 192 students in 11th grade and 196 in 12th grade in one senior high school. Students' responses were categorized using graph models and analyzed in terms of 'Common Activated Knowledge'(CAK). and 'Specific Activated Knowledge'(SAK) across students' cognitive frames, grades, and sex. As contextual differences of the problems increased, context effects in priorities of CAK were reported in favor of laboratory context, on the contrary those of SAK in favor of earth environmental context. Context effects were reported across cognitive frames, especially students with laboratory cognitive frames showed more significant context effects than others. Lower graders and girls showed relatively large context effects. The results of this study showed that science concepts learned in a laboratory context are not easily transferred to earth environmental context. Therefore, special instructional strategies should be developed to overcome the context effect s according to activated knowledges with high priorities in laboratory and earth environmental context.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.174-177
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• 2015

자가 지식 학습 프레임워크는 자연어 텍스트에서 지식 트리플을 생성하기 위한 방법 중 하나로, 문장의 의존 관계 트리 상에서 주어 개체와 목적어 개체 사이의 관계를 패턴으로 학습해 이 패턴을 바탕으로 새로운 지식 트리플을 생성한다. 그러나 이 방법은 의존 관계 트리를 생성하는 도구의 성능에 영향을 받을 뿐만 아니라 생성된 지식 트리플을 반복적으로 사용하는 자가 지식 학습의 특성상 오류가 누적될 가능성이 있다. 이러한 문제점을 해결하기 위해서 본 논문에서는 자가 지식 학습 프레임워크에서 생성된 지식 트리플을 TransR 신뢰도 함수를 사용해 신뢰도 값을 측정하여 그 값에 따라 지식 트리플을 필터링하는 방법을 제안한다. 실험 결과에 따르면 필터링 된 지식 트리플들이 그렇지 않은 지식 트리플들에 비하여 더 높은 정확률을 보여주어, 제안한 방법이 자가 지식 학습 프레임워크의 정확률 향상에 효과적임을 증명하였다.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.119-128
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• 1997

Added variable plot and component-plus-residual plot are very useful for studying the role of a predictor in classical regression analysis. The former is usually used to check the effect of adding a new variable to existing model. The latter has been suggested as computationally convenient substitutes for the added variable plots, however, this plot is found to be better in detecting nonlinear relationships of a new predictor. By combining these two plots dynamically, we can take advantages of two plots simultaneously. And even further, we can get some knowledge of collinearity between a new predictor and predictors already in the model, and more accurate information about the possible outliers.

• Education of Primary School Mathematics
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• v.5 no.2
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• pp.111-119
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• 2001

Over the years, the benefits of instructional manipulatives in mathematics education have been verified by classroom practice and educational research. The purpose of this paper is to introduce how the instructional material, specifically, geoboard could be used and integrated in elementary mathematics classroom in order to develop student's mathematical concepts and process in terms of the following areas: (1) Number '||'&'||' Operation : counting, fraction '||'&'||' additio $n_traction/multiplication (2) Geometry : geometric concepts (3) Geometry : symmetry '||'&'||' motion (4) Measurement : area '||'&'||' perimeter (5) Probability '||'&'||' Statistics : table '||'&'||' graph (6) Pattern : finding patterns Further, future study will continue to foster how manipulatives will enhance children's mathematics knowledge and influence on their mathematics performance.