• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.607-616
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• 2016

In the 2009 revised science curriculum, comprehensive verbs such as 'know (38%)' and 'understand (46%)' are used in more than 80% of the achievement standard. Many readers, such as teachers, textbook makers, etc. have difficulties in interpreting the meaning of achievement standard sentences with these comprehensive verbs. On the other hand, 'Trends in International Mathematics and Science Study (TIMSS)' uses more various and specific verbs to express the cognitive domain. In this study, we analyzed the 2009 revised science curriculum achievement standard focusing on the TIMSS cognitive domain assessment framework. We divided achievement standard to 228 sentences and three teachers analyzed the meaning of verbs in achievement standard. There were two main results of this study. First, the verb 'Know' was analyzed into different kinds of meanings, such as 'Describe (27%)', 'Recall/Recognize (25%)' and 'Relate (17%)', etc; and the verb 'Understand' was analyzed into 'Explain (37%)', 'Relate (27%)' and 'Describe (21%)', etc. Second, there appeared to have a disagreement among the three analysts during the process of interpreting the achievement standards when the level and scope of the contents of each grade is not clear. This study concludes that there's a need for continuous discussion on the use of verbs in achievement standard to promote clearer expressions for better understanding.

• Journal of KIISE:Software and Applications
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• v.26 no.6
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• pp.822-832
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• 1999

개념 그래프는 개념구조(conceptual structures)를 그래프로 표현한 논리 시스템으로 자연어 처리에 적합한 여러 특성을 지니고 있다. 그러나, 개념주조의 그래프 표현은 개념 그래프에 대한 연산의 복잡도를 증가시키고 의미 표현의 일관성(consistency)과 유일성(uniqueness)을 잃게 한다. 본 논문에서는 개념 그래프의 이러한 문제점에 대해 자세히 살펴보고이를 해결한 개념 트리의 표현 방법을 보인다. 표현의 유일성을 위해, 개념 트리는 중심어 선행 표현(perorder expression)에 의해 제약된다 또한 , 표현의 일관성을 위해 집합 참조대상 (set referents)을 확장하고 이를 다루기 위한 set join연산을 재정의한다. 우리는 개념 트리가 자연어 문장에 대한 표현력을 유지하면서 효율적임을 보인다.

• Journal of The Korean Association For Science Education
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• v.35 no.6
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• pp.1031-1038
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• 2015

The list of words and the semantic structure that connects them have been important to the areas of psychology, psychoanalysis, linguistics, and education. Some researchers in constructivist perspectives of science education also have interests in the structure of science concepts expressed by science terminologies. The purpose of this paper was to investigate the test environment factors influencing the word association test as a method to identify students' semantic structures for science terminologies. We set up four variables that are possibly considered in recognizing a word as having scientific meaning. The four variables include: noticing whether stimulus words are science terminologies or not, presenting science terminologies and everyday words alternately, whether presider is science teacher or not, and whether students have learned the concepts or not. In comparing the test results of the experimental group and the control group, we have checked whether each variable influences the test result or not. Stimulus words included nine science terminologies containing both ordinary and scientific meanings, and subjects included 282 middle school students. The degree of recognizing science terminology as having scientific meaning was found to increase only when stimulus words were noticed as science terminologies. In the case of the remaining variables, there was no difference between the control group and the experimental group.

• Journal of the Korean Society for information Management
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• v.19 no.1
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• pp.135-161
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• 2002

This study presents a multilingual integrated ontological approach that enables linking classification systems. thesauri. and terminology databases in science and technology for more effective indexing and information retrieval online. In this integrated system, we designed a thesaurus model with concept as a unit and designated essential data elements for a terminology database on the basis of ISO 12620 standard. The classification system for science and technology adopted in this study provides subject access channels from other existing classification systems through its mapping table. A prototype system was implemented with the field of nuclear energy as an application area.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.562-571
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• 2008

For the purpose of building domain ontology, this paper proposes a methodology for building core ontology first, and then enriching the core ontology with the concepts and relations in the domain thesaurus. First, the top-level concept taxonomy of the core ontology is built using domain dictionary and general domain thesaurus. Then, the concepts of the domain thesaurus are classified into top-level concepts in the core ontology, and relations between broader terms (BT) - narrower terms (NT) and related terms (RT) are classified into semantic relations defined for the core ontology. To classify concepts, a two-step approach is adopted, in which a frequency-based approach is complemented with a similarity-based approach. To classify relations, two techniques are applied: (i) for the case of insufficient training data, a rule-based module is for identifying isa relation out of non-isa ones; a pattern-based approach is for classifying non-taxonomic semantic relations from non-isa. (ii) For the case of sufficient training data, a maximum-entropy model is adopted in the feature-based classification, where k-NN approach is for noisy filtering of training data. A series of experiments show that performances of the proposed systems are quite promising and comparable to judgments by human experts.

• Korean Journal of Cognitive Science
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• v.2 no.1
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• pp.193-204
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• 1990

This paper discusses a reasoning model of information retrieval with a hierarchical thesaurus.The model computes the conceptual distance between a query and an object,both are indexed with weighted terms from a hierarchical thesaurus. The proposed model allows Boolean operators for user queries and edge weights for a hierarchical thesaurus. Experimental results have shown that the proposed model simulates, with surprising accuracy, people in the assessment of conceptual closeness between queries objects.

• Proceedings of the Korea Society of Design Studies Conference
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• 2000.11a
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• pp.132-133
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• 2000

오늘날 메타(mee)라는 용어는 '메타언어', '메타과학', '메타비평' 등의 합성어를 통해 자신의 존재를 이 사회에서 확인 받고 있다. 특히 메타언어는 메타라는 용어를 가장 적극적으로 사용하는 경우라고 할 수 있다. 왜냐하면 메타언어는 언어를 연구대상으로 하는 학문영역에서 언어의 한 특성, 흑은 자체의 구조를 설명하는 보다 일반화된 개념으로 소통되고 있기 때문이다. (중략)

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.48-460
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• 2001

국어사전의 뜻풀이말은 표제어의 의미를 기술할 뿐만 아니라, 상위/하위개념, 부분-전체개념, 다의어, 동형이의어, 동의어, 반의어, 의미속성 등의 많은 의미정보를 내재하고 있다. 본 연구는 뜻풀이말에서 다양한 의미정보를 획득을 위한 기본적인 도구로서 국어사전의 뜻풀이말 구문분석기를 구현하는 것을 목적으로 한다. 이를 위해서 우선 국어사전의 뜻풀이말을 대상으로 일정한 수준의 품사 및 구문 부착 말 뭉치를 구축하고, 이 말뭉치들로부터 품사 태그 중의성 어절의 빈도 정보와 통계적 방법에 기반한 문법규칙과 확률정보를 자동으로 추출한다. 본 연구의 뜻풀이말 구문분석기는 이를 이용한 확률적 차트파서이다. 품사 태그 중의성 어절의 빈도 정보와 문법규칙 및 확률정보는 파싱 과정의 명사구 중의성을 해소한다. 또한, 파싱 과정에서 생성되는 노드의 수를 줄이고 수행 속도를 높이기 위한 방법으로 문법 Factoring, Best-First 탐색 그리고 Viterbi 탐색의 방법을 이용한다. 문법규칙의 확률과 왼쪽 우선 파싱 그리고 왼쪽 우선 탐색 방법을 사용하여 실험한 결과, 왼쪽 우선 탐색 방식과 문법확률을 혼용하는 방식이 가장 정확한 결과를 보였으며 비학습 문장에 대해 51.74%의 재현률과 87.47%의 정확률을 보였다.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.41-51
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• 2002

In this paper, I describe a knowledge-based question-answering system and significance of the system with a view to constructing a fact database. The knowledge-based system takes advantage of existing NLP-resources such as conceptual structures of ontologies along with morphotogical, syntactic and semantic analysis. The use of conceptual structures allows us to select right answers through inferences basically made by expansions of concepts. However, the work of constructing factual knowledge requires a great amount of acquisition time in large-scale applications because of the nature of human interference. This is why the procedure of acquiring factual knowledge cannot be fully automated. Apart from efficiency considerations. the knowledge-based system deserves serious consideration, I point out benefits of the system and describe the whole procedure of building the system in terms of a fact database.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.747-751
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• 2010

This paper proposes the method that increases the accuracy for tagging word meaning by creating sense tagged data automatically using machine readable dictionaries. The concept of meaning group is applied here, where the meaning group for each meaning of a target word consists of neighbor words of the target word. To enhance the tagging accuracy, the notion of concentration is used for the weight of each word in a meaning group. The tagging result in SENSEVAL-2 data shows that accuracy of the proposed method is better than that of existing ones.