• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.101-109
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• 2013

Data quality management is extremely important. In this study, we proposed data standardization for effective quality management of enterprise-owned data about distributed goods and validated its effectiveness by case study. For the standardization of data, we designed data category and data dictionary. Additionally, we categorized data and identified its attributes for data category design, and we developed design process for data dictionary and built the dictionary of word, term, domain and code for data dictionary design. And then we proposed output documents which have to be written for data standardization. Proposed data standardization approach is validated its efficiency by quantitative and qualitative measurement. and as a result the data quality of the data standardization improved 24% and the data quality of the consistency of the data dictionary improved 7%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.145-155
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• 2010

In this paper, we suggest a fish drug domain ontology to show an associated information and hierarchy together through concept-relation and inference mechanism instead of keyword matching. First, we investigate competency questions from workers of fishery industry and then we derive concepts and terminologies. Next, we present a process of fish drug ontology modelling using Protege-OWL editor, which is an extension of Protege that supports the Web Ontology Language(OWL). Last, we suggest the user interface of semantic web-based fish drug information system to search easily associated informations of fish drug using this ontology. It is to provide an effective search method that fish disease manager, fish farmer, and students majoring in fisheries can confirm details of diseases, fish, and drug evaluations associated with fish drug within one screen without moving to another position.

• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.29-39
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• 2006

The Web is evolving quantitatively into an explosive development. However, users usually have heavy burden of searching information because of the absence of contextual meaning on the Web. Due to an enormous amount of information, users have to endure for finding strong cohesive keywords by themselves and read each of the documents with enduring effort. This paper proposes an efficient method of searching more relative documents than current KEM-based searching systems on the Web by using contextual meaning. We designed a domain ontology on computer hardware, and a searching system which was searching those e-Learning objects. Owing to the Ontology-applied search system, information such as educational materials and related multimedia can be easily provided to the users. Further, learners could be informed of relationship of knowledge, e.g., class hierarchy, properties and values, and so on. The request results are semantically related to users' needs, and thus the system provides a learner-centered searching.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.396-405
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• 2005

Since sentences are the basic propositional units of text, their themes would be helpful for various tasks that require knowledge about the semantic content of text. Despite the importance of determining the theme of a sentence, however, few studies have investigated the problem of automatically assigning the theme to a sentence. Therefore, we propose a sentence theme allocation scheme based on the head-driven patterns of sentences in encyclopedia. In a serious of experiments using Dusan Dong-A encyclopedia, the proposed method outperformed the baseline of the theme allocation performance. The head-driven pattern 4, which is reconfigured based on the predicate, showed superior performance in the theme allocation with the average F-score of $98.96\%$ for the training data, and $88.57\%$ for the test data.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.278-288
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• 2002

This paper presents a design and implementation of fuzzy retrieval system that could support an associated learning in problem banks. It tries to retrieve some of the problems conceptually related to specific semantics described by user's queries. In particular, the problem retrieval system employs a fuzzy thesaurus which represents relationships between domain dependent vocabularies as fuzzy degrees. It would keep track of characteristics of the associated learning, which should guarantee high recall and acceptable precision for retrieval effectiveness. That is, since the thesaurus could make a vocabulary mismatch problem resolved among query terms and document index terms, this retrieval system could take a chance to effectively support user's associated teaming. Finally, we have evaluated whether the fuzzy retrieval system is appropriate for the associated teaming or not, by means of its precision and recall rate point of view.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.716-729
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• 2006

The information systems in the most enterprise environments are distributed locally and are comprised with various heterogeneous data sources, so that it is difficult to obtain necessary and integrated information for supporting user decision. For solving 'this problems efficiently, it provides uniform interface to users and constructed database systems between heterogeneous systems make a consistence each independence and need to provide transparency like one interface. This paper presents XMDR that consists of category, standard ontology, location ontology and knowledge base. Standard ontology solves heterogeneous problem about naming, attributes, relations in data expression. Location ontology is a mediator that connects each legacy systems. Knowledge base defines the relation for sharing glossary. Adaptive retrieve proposes integrated retrieve system through reflecting site weight by location ontology, information sharing of various forms of knowledge base and integration and propose conceptual domain model about how to share unstructured knowledge.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.350-354
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• 2010

전 세계적인 지구 온난화 현상으로 해수면 상승과 생태계 변화가 발생하여 기후변화에 대한 사회적관심이 증가하고 있다. 이와 더불어 기후변화와 지구환경시스템의 대기, 수권, 생물권, 지표면 동 각 권역간의 상호작용과 피드백을 고려한 연구가 증가하고 있는 실정이다. 기후와 환경을 통합적으로 분석하여 기후변화에 따른 지구환경시스템의 변화특성을 이해하고 이러한 피드백 과정을 파악하기 위해서는 분석 자료의 원활한 공유와 연계를 위한 통합 데이터베이스 구축이 필요하다. 이를 위해서는 먼저 다양한 기후/환경 연구 분야의 자료를 관리하기 위한 데이터의 의미, 명칭, 정의 등에 대한 원칙의 수립이 요구된다. 따라서 본 연구에서는 기후/환경 변화예측 연구 자료의 원활한 공유와 관리를 위한 데이터 표준화 연구를 수행하였다. 기후/환경 변화예측 연구 분야의 자료 현황을 조사 및 분석하였고 그에 따른 자료 관리 방안을 마련하였다. 그 결과 관리할 오브젝트를 기준으로 기후/환경 연구 분야의 데이터 표준화를 수행하였고 표준단어, 표준도메인, 표준용어를 정의하였다. 데이터 표준화 결과는 기후/환경 변화예측 자료를 관리하고 공유하는데 있어 데이터의 의미를 효율적으로 파악하고, 데이터베이스 설계과정에서 데이터의 품질과 생산성을 향상 시킬 수 있다. 향후 연구에서는 데이터베이스 개념적 엔티티의 속성설계 단계부터 데이터 표준을 적용한 통합 데이터베이스 구축이 필요하다.

• Journal of KIISE
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• v.43 no.4
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• pp.430-436
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• 2016

Processing of unknown words such as proper nouns and newly coined words is important for a morphological analyzer to process documents in various domains. In this study, a segmentation and tagging method for unknown Korean words is proposed for the 3-step probabilistic morphological analysis. For guessing unknown word, it uses rich suffixes that are attached to open class words, such as general nouns and proper nouns. We propose a method to learn the suffix patterns from a morpheme tagged corpus, and calculate their probabilities for unknown open word segmentation and tagging in the probabilistic morphological analysis model. Results of the experiment showed that the performance of unknown word processing is greatly improved in the documents containing many unregistered words.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.93-100
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• 2022

Keyword-oriented search methods are mainly used as data search methods, but this is not suitable as a search method in the legal field where professional terms are widely used. In response, this paper proposes an effective data search method in the legal field. We describe embedding methods optimized for determining similarities between sentences in the field of natural language processing of legal domains. After embedding legal sentences based on keywords using TF-IDF or semantic embedding using Universal Sentence Encoder, we propose an optimal way to search for data by combining BERT models to check similarities between sentences in the legal field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.123-125
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• 2024

다크웹(Dark Web)은 마약, 불법 촬영물, 해킹, 무기 등 불법 콘텐츠의 공유 및 거래가 이루어지는 인터넷 영역으로서 최근에는 소셜미디어와 연계된 형태로 범죄 양상이 변화하고 있다. 본 논문에서는 최근 국내 외 사회 문제로 대두되고 있는 마약 범죄를 추적하기 위한 다크웹 수사 기술로서 다크웹 사용자를 소셜미디어로 유인하는 마약 정보 수집체계를 개발한다. 먼저 미국 마약단속국에서 공개한 대표적인 마약 용어 3개의 표준어 및 은어를 검색 키워드로 사용하여 마약 관련 다크웹을 수집하고, 수집된 다크웹을 크롤링하여 소셜미디어 계정 링크를 추출한다. 본 논문에서는 다양한 소셜미디어 중, 트위터 및 텔레그램 접속 링크를 수집하였으며 실험 결과, 접속 가능한 총 54개 다크웹 도메인의 9,046개 웹 페이지에서 트위터 유인 링크 567개, 텔레그램 유인 링크 118개를 추출하였다.