• Title/Summary/Keyword: Knowledge Mapping

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The Effects of Using Concept Mapping as an Instructional Tool in Elementary School Science Classes (초등학교 과학 수업에서 개념도 활용의 효과)

  • 강석진;이유영;고한중;전경문;노태희
    • Journal of Korean Elementary Science Education
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    • v.23 no.1
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    • pp.37-43
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    • 2004
  • In this study, we investigated the effects of using concept mapping as an instructional tool on 4th graders' achievement, science learning motivation, and attitude toward science classes. Two classes (38 students) from an elementary school were respectively assigned to a control group and a treatment group. Students were taught about "expansion of matter by heat" and "heat transfer". These topics were chosen because they require students to understand more concepts as well as relationships among them. A science learning motivation test and an attitude toward science classes test were administered as pretests. A researcher-made achievement test, the science learning motivation test, and the attitude toward science classes test were administered as posttests. The results indicated that using concept mapping in 4th-grade science classes was not significantly effective in improving students' achievement though a statistically significant positive effect was found in the subcategory of knowledge. No statistically significant effect of using concept mapping was found in the scores of the science learning motivation test and the attitude toward science classes test. Educational implications are discussed.

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APAS:Aerial Photograph Analysis System (항공 사진 분석 시스템)

  • 김범수;김병천
    • Korean Journal of Cognitive Science
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    • v.2 no.2
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    • pp.359-403
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    • 1990
  • This paper introduces a blackboard system which extracts imbedded road and building structures irom aerial photograph images. The role of three major component(blackboard, knowledge source, and control module)in blackboard system will be illustrated in terms of knowledge representation and control strategies. The hypothesis on a blackboard will be organized in a hierarchical form, the knowledge sources which generate hypothesis and verify them will be shown in detail, and the control module will describe how the knowledge sources can dervie solutions. Especially this paper shows that searching image strutures can be greatly simplified by the use of a mapping image.

Knowledge Mapping of Robotic Applications in Tourism and Hospitality

  • Huiyue, Ye;Sirong, Chen;Rob, Law;Lawrence Hoc Nang, Fong
    • Journal of Smart Tourism
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    • v.2 no.4
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    • pp.11-23
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    • 2022
  • The use of robots in tourism and hospitality contexts have drawn increasing scholarly and practical regard. Although the number of recent robotics related studies continue to grow, a general knowledge map, which is important to point out promising directions for future studies, remains to be made. To understand the application of robotics in tourism and hospitality, this study conducts descriptive and bibliometric analysis to present a holistic knowledge map of this specific field where research trend, key contributors, highly cited references, and popular themes were identified. Collaboration networks among institutes and regions were additionally illustrated. Collaboration across fields, industries, and perspectives were encouraged following the findings and both theoretical and practical implications are accordingly provided.

Implementation of Artificial Intelligence Systems for Agri-food Supply Chains: A Bibliometric Approach

  • Javier RAMIREZ;Henry HERRERA;Osman REDONDO;Sofia SULBARAN
    • Journal of Distribution Science
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    • v.22 no.6
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    • pp.83-93
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    • 2024
  • Purpose: The present study is developed with the aim of mapping the trends in scientific production related to the implementation of artificial intelligence systems for agro-food supply chains. Research design, data and methodology: The methodological approach of the research shows a descriptive documentary process based on bibliometric techniques for mapping the main indicators of the area of knowledge through the establishment of a search equation in Scopus. Results: The research results show a total of 633 documents published between 2019 and 2023, with a great annual growth rate of 61.68%; In addition to a notable participation of countries such as India, China, the United Kingdom and the United States in the generation of new knowledge related to artificial intelligence applied to food distribution systems. Conclusions: It is concluded that the rise of new artificial intelligence technologies has shown extremely important results in the development of industries worldwide, with increasingly accelerated steps; which certainly translates into the creation of spaces and incentives in the production of research aimed at understanding these dynamics and in turn to propose new alternatives and proposals for the reduction of the large technological gaps that are present within the agro-food sector.

Knowledge Extraction Methodology and Framework from Wikipedia Articles for Construction of Knowledge-Base (지식베이스 구축을 위한 한국어 위키피디아의 학습 기반 지식추출 방법론 및 플랫폼 연구)

  • Kim, JaeHun;Lee, Myungjin
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.43-61
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    • 2019
  • Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

Features, Functions and Components of a Library Classification System in the LIS tradition for the e-Environment

  • Satija, M.P.;Martinez-Avila, Daniel
    • Journal of Information Science Theory and Practice
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    • v.3 no.4
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    • pp.62-77
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    • 2015
  • This paper describes qualities of a library classification system that are commonly discussed in the LIS tradition and literature, and explains such a system’s three main functions, namely knowledge mapping, information retrieval, and shelf arrangement. In this vein, the paper states the functional requirements of bibliographic classifications, which broadly are subject collocation and facilitation of browsing the collection. It explains with details the components of a library classification system and their functions. The major components are schedules, notations, and index. It also states their distinguished features, such as generalia class, form divisions, book numbers, and devices for number synthesis which are not required in a knowledge classification. It illustrates with examples from the WebDewey good examples of added features of an online library classification system. It emphasizes that institutional backup and a revision machinery are essential for a classification to survive and remain relevant in the print and e-environment.

Development of Case-adaptation Algorithm using Genetic Algorithm and Artificial Neural Networks

  • Han, Sang-Min;Yang, Young-Soon
    • Journal of Ship and Ocean Technology
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    • v.5 no.3
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    • pp.27-35
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    • 2001
  • In this research, hybrid method with case-based reasoning and rule-based reasoning is applied. Using case-based reasoning, design experts'experience and know-how are effectively represented in order to obtain a proper configuration of midship section in the initial ship design stage. Since there is not sufficient domain knowledge available to us, traditional case-adaptation algorithms cannot be applied to our problem, i.e., creating the configuration of midship section. Thus, new case-adaptation algorithms not requiring any domain knowledge are developed antral applied to our problem. Using the knowledge representation of DnV rules, rule-based reasoning can perform deductive inference in order to obtain the scantling of midship section efficiently. The results from the case-based reasoning and the rule-based reasoning are examined by comparing the results with various conventional methods. And the reasonability of our results is verified by comparing the results wish actual values from parent ship.

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Analysis of the Conceptual Map of Kindergarten Teachers Concerning the Content of Music Instruction (유아음악교육내용에 대한 교사의 개념도 분석)

  • Sim, Seong Kyung;Yi, Hyo Sook;Yim, Sun Ok;Park, Sun Yi;Heo, Eun Ju;Park, Ji Ae
    • Korean Journal of Child Studies
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    • v.24 no.4
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    • pp.71-88
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    • 2003
  • Concept mapping was used to analyze the knowledge of kindergarten teachers about early childhood music instructional content. Data obtained from the 85 subjects was analyzed by Yun's method(1998) based on Novak & Gowin(1984), Morine-Dershimer(1993), and Markhan, Mintzes & Jones(1994). The majority of the teachers perceived the superordinate concepts of early childhood music instructional content to be listening to music, singing, movement, and playing musical instruments. They perceived early childhood music instructional content to be activity rather then knowledge. Listening to music was high in frequency among superordinate concepts and musical attitudes were high among subordinate concepts. Teachers used 285 words in expressing their cognitive maps. There was no effect on cognitive maps by teaching career or level of education.

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Knowledge-based learning for modeling concrete compressive strength using genetic programming

  • Tsai, Hsing-Chih;Liao, Min-Chih
    • Computers and Concrete
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    • v.23 no.4
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    • pp.255-265
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    • 2019
  • The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

Morphological Classification of Knowledge Map for Science and Technology and Development of Knowledge Map Examples in the View of Information Analysis (과학기술 지식맵의 형태적 분류와 정보분석 관점의 지식맵 사례 도출)

  • Lee, Bangrae;Lee, June Young;Kim, Dohyun;Noh, Kyung Ran;Yang, Myung Seok;Kwon, Oh-Jin;Choi, Kwang-Nam;Kim, Han-Joon
    • The Journal of the Korea Contents Association
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    • v.13 no.11
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    • pp.461-476
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    • 2013
  • Knowledge maps for science and technology are used extensively in the research projects. However, they are not organized systematically and are not necessarily suitable to be used in the research projects. Therefore, this study aims to organize the knowledge maps in order to support scientific research projects. To this end, the existing knowledge maps for science and technology are classified as one of four types based on data representation methods; the frequency summary map, trend summary map, distribution-based knowledge map and network-based knowledge map. Additionally, by summarizing and classifying the knowledge maps through the principle of 'five w's and one h', the unexplored area are investigated. Finally, some examples of useful knowledge maps in terms of data analysis are provided with details such as definitions, components and utilization purposes. These findings may be a starting point for future research into a better understanding of knowledge maps for science and technology.