• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.67-69
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• 2005

본 논문에서는 기 구축된 전자 카탈로그를 의미적 연결 관계에 기초한 확장된 전자 카탈로그로 변환하는 방법을 제안한다. 이를 통해 구축된 확장된 전자 카탈로그에서 의미적 태깅에 의한 확장된 어휘 인덱스 구축 방안과, 이를 이용한 검색 성능 향상 기법을 제안한다. 기존의 전자 카탈로그는 상품 정보가 분류별로 생성된 테이블에 저장되고 저장된 테이블로부터 생성된 키워드 인덱스로부터 검색이 이루어 졌다. 이러한 검색은 상품이 가지는 정보를 데이터베이스에 구축된 테이블에만 한정하게 되어 전자 카탈로그에 포함된 상품이나 분류간의 의미적 연결 관계들을 충분히 이용하지 못하였다 전자 카탈로그에 내재된 의미적 요소를 충분히 활용하기 위해서는 전자 카탈로그를 의미적 연결 관계에 기초한 모델로 구성할 필요가 있다. 본 논문에서는 의미적 모델 기반 전자 카탈로그 시스템으로의 전환 과정을 XML형태의 명세를 이용해 반자동적으로 전환할 수 있는 툴을 구현하며, 단순 키워드 어휘 인덱스 구축이 아닌, 어휘 인덱스의 의미적 확장을 제안하고, 이를 위한 태그 요소로써 어휘에 대한 형태소 분석 결과, 수치 환산 및 확장 요소, 속성간의 도메인 정보 등을 제시하였다. 이를 기반으로 최적의 검색 결과를 얻어 내도록 하는 인접도 평가 함수에 적용하는 방법을 제시한다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2075-2082
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• 1998

Rule-based knowledge representHtion is, the most popular technique for ,storage and manipulation of domain knowledge in expert system. By the way, the amount of knowledge increases more and more in this representatiun technique, it, relationship becomes complex, and even its contents can be modified. This is the reason why rule-based knowledge representation technique requires a verification ,system which can maintain consistency and completeness of knowledge base. This paper is to propose a verification system for consistency and completeness of knowledge base to promote the efficiency and reliability of expert system. After verifying the potential errors both in structure and in semantics whenever a new rule is added, this system renders knowledge base consistent and complete by correcting them automatically or by making expert correct them if it fails.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.466-475
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• 2009

Considerable research in the field of ontology mapping has been done when information sharing and reuse becomes necessary by a variety of ontology development. Ontology mapping method consists of the lexical, structural, instance, and logical inference similarity computing. Lexical similarity computing used in most ontology mapping methods performs an ontology mapping by using the synonym set defined in the WordNet. In this paper, we define the Super Word Set including the hypenym, hyponym, holonym, and meronym set and propose an ontology mapping method using the Super Word Set. The results of experiments show that our method improves the performance by up to 12%, compared with previous ontology mapping method.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.105-113
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• 2014

In this paper, we propose an OWL DL mapping rules for construction legal ontology based on the analyzed relationship between the structural features and elements of the statute. The mapping rule to be proposed is the method building the structure of the domestic statute, unique attribute of the statute, and reference relation between laws with TBox, and the legal sentence is analyzed, and the pattern type of the sentence is selected. It expresses with ABox. The proposed mapping rule is transformed to the information in which the computer can process the domestic legal document. It is usable for the legal knowledge base.

• Journal of Daesoon Thought and the Religions of East Asia
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• v.1 no.1
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• pp.103-125
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• 2021

This paper deals with figurative meanings of Haewon-sangsaeng. It is an investigation which is both semantic and diachronic. In the first part, important implications of sangsaeng (or xiangsheng in Chinese) in the context of correlative cosmology are extensively explored. Among others, saeng (in Chinese sheng) as a powerful metaphor and its related Chinese compounds are broadly discussed. In the second part, the evolution of ideas of yuan (or won in Korean) in Chinese history is explicated. Above all, in the traditional Chinese cultural milieu, wrongful treatments which make victims feel themselves aggrieved are socio-politically orientated. The Scripture on Great Peace (Taiping Jing) is used as reference point to elucidate the essential points of yuan and its knots. However, the advent of Buddhism in East Asia adds a new dimension to the understanding of yuan (won). Accumulated yuan as karmic bond thus gives a new identity of yuan as predetermined animosity. Widely recognized idioms like "adverse relatives and karmic debtors" and indigenous Chinese Buddhist rituals like Repentance Ritual of the Emperor Liang bear witness to this transformation of the meaning of yuan in East Asia. The fruitful yet correlated meanings of yuan also make the endeavor of untying yuan deeply significant and important to human society. Haewon-sangsaeng, as a religio-ethical ideal, brings out an amicable and harmonious relationship among myriad beings in the cosmos.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.66-71
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• 2021

The purpose of this paper is to identify keywords related to museums, gamification, and visitors, and provide basic data that the museum market can be expanded by using gamification. That used to collect data for blogs, news, cafes, intellectuals, academic information by Naver and Daum which is Web documents in Korea, and Google Web, news, Facebook, Baidu, YouTube, and Twitter for analysis. For the data analysis period, a total of one year of data was selected from April 16, 2020 to April 16, 2021, after Corona. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analysed and visualized using the Netdraw function in the UCINET6 program. In addition, We performed CONCOR analysis to derive clusters for similar keywords. As a result, a total of 25,761 cases that analysing the keywords of museum, gamification and visitors were derived. This shows that the museum, gamification, and spectators are related to each other. Furthermore, if a system using gamification is developed for museums, the museum market can be developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.614-627
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• 2022

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.49-57
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• 2010

This paper proposes a Korean document. categorization algorithm using Back Propagation Neural Network(BPNN) with Singular Value Decomposition(SVD). BPNN makes a network through its learning process and classifies documents using the network. The main difficulty in the application of BPNN to document categorization is high dimensionality of the feature space of the input documents. SVD projects the original high dimensional vector into low dimensional vector, makes the important associative relationship between terms and constructs the semantic vector space. The categorization algorithm is tested and compared on HKIB-20000/HKIB-40075 Korean Text Categorization Test Collections. Experimental results show that BPNN algorithm with SVD achieves high effectiveness for Korean document categorization.