• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1035-1043
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• 2018

There is a case where the core content of another person's work is decorated as though it is his own thoughts by changing own thoughts without showing the source. Plagiarism test of copykiller free service used in plagiarism check is performed by comparing plagiarism more than 6th word. However, it is not enough to judge it as a plagiarism with a six - word match if it is replaced with a similar word. Therefore, in this paper, we construct word clusters by using DBSCAN algorithm, find synonyms, convert the words in the clusters into representative synonyms, and construct L-R tables through L-R parsing. We then propose a method for determining the similarity of documents by applying weights to the thesaurus and weights for each paragraph of the thesis.

• 한국디지털정책학회:학술대회논문집
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• 2007.06a
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• pp.79-87
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• 2007

This paper aims to offer a solution based on semantic document classification to improve e-Government utilization and efficiency for people using their own information retrieval system and linguistic expression Generally, semantic document classification method is an approach that classifies documents based on the diverse relationships between keywords in a document without fully describing hierarchial concepts between keywords. Our approach considers the deep meanings within the context of the document and radically enhances the information retrieval performance. Concept Weight Document Classification(CoWDC) method, which goes beyond using exist ing keyword and simple thesaurus/ontology methods by fully considering the concept hierarchy of various concepts is proposed, experimented, and evaluated. With the recognition that in order to verify the superiority of the semantic retrieval technology through test results of the CoWDC and efficiently integrate it into the e-Government, creation of a thesaurus, management of the operating system, expansion of the knowledge base and improvements in search service and accuracy at the national level were needed.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.22-28
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• 2008

This paper aims to offer a solution based on semantic document classification to improve e-Government utilization and efficiency for people using their own information retrieval system and linguistic expression. Generally, semantic document classification method is an approach that classifies documents based on the diverse relationships between keywords in a document without fully describing hierarchial concepts between keywords. Our approach considers the deep meanings within the context of the document and radically enhances the information retrieval performance. Concept Weight Document Classification(CoWDC) method, which goes beyond using existing keyword and simple thesaurus/ontology methods by fully considering the concept hierarchy of various concepts is proposed, experimented, and evaluated. With the recognition that in order to verify the superiority of the semantic retrieval technology through test results of the CoWDC and efficiently integrate it into the e-Government, creation of a thesaurus, management of the operating system, expansion of the knowledge base and improvements in search service and accuracy at the national level were needed.

• Journal of Digital Convergence
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• v.6 no.4
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• pp.177-183
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• 2008

This paper aims to offer a solution based on intelligent document classification to create a user-centric information retrieval system allowing user-centric linguistic expression. So, structures expressing user intention and fine document classifying process using EBL, similarity, knowledge base, user intention, are proposed. To overcome the problem requiring huge and exact semantic information, a hybrid process is designed integrating keyword, thesaurus, probability and user intention information. User intention tree hierarchy is build and a method of extracting group intention between key words and user intentions is proposed. These structures and processes are implemented in HDCI(Hybrid Document Classification with Intention) system. HDCI consists of analyzing user intention and classifying web documents stages. Classifying stage is composed of knowledge base process, similarity process and hybrid coordinating process. With the help of user intention related structures and hybrid coordinating process, HDCI can efficiently categorize web documents in according to user's complex linguistic expression with small priori information.

• Archives of design research
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• v.14 no.4
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• pp.5-14
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• 2001

Among Human Sensibility-oriented products a gap between the images that designers try to express through that product and users emotional evaluation becomes an issue. The data on the correlation between image words used for design evaluation and images used in the design process are especially significant. This study based on these correlations suggests a Fuzzy retrieval system supporting styling design with images and image words. In the system, the relational data are demonstrated by Fuzzy thesaurus as correlation coefficient from the degree of similarity among image words. And the degree of similarity is produced based on image evaluation. Image retrieval is conducted by the algorithm of Fuzzy thesaurus development, 1) among image words, 2) images to image words, 3) image words to images and 4) among images: 4 different modes are provided as retrieval modes. Also transfer between modes is carried by direct operating interface, therefore divergent thinking and convergent thinking is supported well. The system consists of operation for the gap and the measurement unit of emotional evaluation, and visualization units. Under unified interface environments are set in order for consistency of the operation.

• Journal of Computing Science and Engineering
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• v.2 no.1
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• pp.26-43
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• 2008

We propose a new query expansion method in the extended Boolean model that improves precision without degrading recall. For improving precision, our method promotes the ranks of documents having more query terms since users typically prefer such documents. The proposed method consists of the following three steps: (1) expanding the query by adding new terms related to each term of the query, (2) further expanding the query by adding augmented terms, which are conjunctions of the terms, (3) assigning a weight on each term so that augmented terms have higher weights than the other terms. We conduct extensive experiments to show the effectiveness of the proposed method. The experimental results show that the proposed method improves precision by up to 102% for the TREC-6 data compared with the existing query expansion method using a thesaurus proposed by Kwon et al.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.292-297
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• 2008

복잡한 시맨틱을 포함한 웹 문서를 정확히 범주화하고 이 과정을 자동화하기 위해서는 인간의 지식체계를 수용할 수 있는 표준화, 지능화, 자동화된 문서표현 및 분류기술이 필요하다. 이를 위해 키워드 빈도수, 문서내 키워드들의 관련성, 시소러스의 활용, 확률기법 적용 등에 사용자의도(intention) 정보를 활용한 범주화와 조정 프로세스를 도입하였다. 웹 문서 분류과정에서 시소러스 등을 사용하는 지식베이스 문서분류와 비 감독 학습을 하는 사전 지식체계(a priori)가 없는 유사성 문서분류 방법에 의도정보를 사용할 수 있도록 기반체계를 설계하였고 다시 이 두 방법의 차이는 Hybrid조정프로세스에서 조정하였다. 본 연구에서 설계된 HDCI(Hybrid Document Classification with Intention) 모델은 위의 웹 문서 분류과정과 이를 제어 및 보조하는 사용자 의도 분석과정으로 구성되어 있다. 의도분석과정에 키워드와 함께 제공된 사용자 의도는 도메인 지식(domain Knowledge)을 이용하여 의도간 계층트리(intention hierarchy tree)를 구성하고 이는 문서 분류시 제약(constraint) 또는 가이드의 역할로 사용자 의도 프로파일(profile) 또는 문서 특성 대표 키워드를 추출하게 된다. HDCI는 문서간 유사성에 근거한 상향식(bottom-up)의 확률적인 접근에서 통제 및 안내의 역할을 수행하고 지식베이스(시소러스) 접근 방식에서 다양성에 한계가 있는 키워들 간 관계설정의 정확도를 높인다.

• Journal of KIISE:Software and Applications
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• v.26 no.8
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• pp.964-973
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• 1999

본 논문에서는 퍼지 트리플을 사용하는 내용 기반 이미지 검색 방법을 제안한다. 이미지 내 객체들 사이의 공간 관계는 내용 기반 이미지 검색을 위해 사용되는 주요한 속성들 중의 하나이다. 그러나, 기존의 트리플을 이용한 이미지 검색 시스템들은 개념 기반 검색 방법을 지원하지 못하고, 방향들 사이의 근사 정합을 처리하지 못하는 문제점을 가지고 있다. 이 문제를 해결하기 위하여 본 논문에서는 개념 기반 정합과 근사 정합을 지원하는 퍼지 트리플을 이용한 이미지 검색 방법을 제안한다. 개념 기반 정합을 위해서는 퍼지 소속성 집합으로 이루어진 시소러스가 사용되며, 근사 정합을 위해서는 방향들 사이의 관계를 정량화 하기 위한 k-weight 함수가 각각 이용된다. 이 두 가지 정합은 퍼지 트리플 간의 퍼지 정합을 통하여 균일하게 지원될 수 있다. 본 논문에서는 또한, 개념 기반 정합과 근사 정합에 대한 검색 효과를 정량적으로 평가하는 작업을 수행한다. Abstract This paper proposes an inexact and a concept-based image match technique based on fuzzy triples. The most general method adopted to index and retrieve images based on this spatial structure may be triple framework. However, there are two significant drawbacks in this framework; one is that it can not support a concept-based image retrieval and the other is that it fails to deal with an inexact match among directions. To compensate these problems, we develope an image retrieval technique based on fuzzy triples to make the inexact and concept-based match possible. For the concept-based match, we employ a set of fuzzy membership functions structured like a thesaurus, whereas for the inexact match, we introduce k-weight functions to quantify the similarity between directions. In fuzzy triples, the two facilities are uniformly supported by fuzzy matching. In addition, we analyze the retrieval effectiveness of our framework regarding the degree of the conceptual matching and the inexact matching.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.491-500
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• 2004

This paper presents an ontology construction method using various computational language resources, and an ontology-based word sense disambiguation method. In order to acquire a reasonably practical ontology the Kadokawa thesaurus is extended by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. To apply the ontology to disambiguate word senses, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. The mutual information between concepts in the ontology was calculated before using the ontology as knowledge for disambiguating word senses. If mutual information is regarded as a weight between ontology concepts, the ontology can be treated as a graph with weighted edges, and then we locate the weighted path from one concept to the other concept. In our practical machine translation system, our word sense disambiguation method achieved a 9% improvement over methods which do not use ontology for Korean translation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1698-1711
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• 2011

Knowledge is often represented as a set of rules or a semantic network in intelligent systems. Recently, ontology has been widely used to represent semantic knowledge, because it organizes thesaurus and hierarchal information between concepts in a particular domain. However, it is not easy to collect semantic relationships among concepts. Much time and expense are incurred in ontology construction. Collective intelligence can be a good alternative approach to solve these problems. In this paper, we propose a collective intelligence approach of Games With A Purpose (GWAP) to collect various semantic resources, such as words and word-senses. We detail how to construct the semantic information model or ontology from the collected semantic resources, constructing a system named FunWords. FunWords is a Korean lexical-based semantic resource collection tool. Experiments demonstrated the resources were grouped as common nouns, abstract nouns, adjective and neologism. Finally, we analyzed their characteristics, acquiring the semantic relationships noted above. Common nouns, with structural semantic relationships, such as hypernym and hyponym, are highlighted. Abstract nouns, with descriptive and characteristic semantic relationships, such as synonym and antonym are underlined. Adjectives, with such semantic relationships, as description and status, illustration - for example, color and sound - are expressed more. Last, neologism, with the semantic relationships, such as description and characteristics, are emphasized. Weighting the semantic relationships with these characteristics can help reduce time and cost, because it need not consider unnecessary or slightly related factors. This can improve the expressive power, such as readability, concentrating on the weighted characteristics. Our proposal to collect semantic resources from the collective intelligence approach of GWAP (our FunWords) and to weight their semantic relationship can help construct the semantic information model or ontology would be a more effective and expressive alternative.