• Journal of the Korean Society for information Management
• /
• v.29 no.4
• /
• pp.101-121
• /
• 2012

Since IR systems which adopt only Boolean IR model can not provide ranked search result, users have to conduct time-consuming checking process for huge result sets one by one. This study proposes a method to provide search results ranked by using coupling information between tags instead of index weight information in Boolean IR model. Because document queries are used instead of general user queries in the proposed method, key tags used as queries in a relevant document are extracted. A variety of groups of Boolean queries based on tag couplings are created in the process of extracting queries. Ranked search result can be extracted through the process of matching conducted with differential information among the query groups and tag significance information. To prove the usability of the proposed method, the experiment was conducted to find research trend analysis information on selected research information. Aslo, the service based on the proposed methods was provided to get user feedback for a year. The result showed high user satisfaction.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.5
• /
• pp.596-607
• /
• 2017

This study examined the method for adding related to "stomach neoplasms" as filters to the Medical Subject Headings (MeSH) for search as well as a method for improving the search efficiency through a word-proximity search by measuring the distance of co-occurring terms. A total of 8,625 articles published between 2007 and 2016 with the major topic terms "stomach neoplasms" were downloaded from PubMed article titles. The vocabulary to be added to the MeSH for search were analyzed. The search efficiency was verified by 277 articles that had "Stomach Neoplasms" indexed as MEDLINE MeSH in KoreaMed. As a result, 973 terms were selected as the candidate vocabulary. "Gastric Cancer" (2,780 appearances) was the most frequent term and 7,376 compound words (88.51%) combined the histological terms of "stomach" and "neoplasm", such as "gastric adenocarcinoma" and "gastric MALT lymphoma". A total of 5,234 compounds words (70.95%), in which the co-occurring distance was two words, were found. The matching rate through the MEDLINE MeSH and KoreaMed MeSH Indexer was 209 articles (75.5%). The search efficiency improved to 263 articles (94.9%) when the search filters were added, and to 268 articles (96.7%) when the 13 word-proximity search technique of the co-occurring terms was applied. This study showed that the use of a thesaurus as a means of improving the search efficiency in a natural language search could maintain the advantages of controlled vocabulary. The search accuracy can be improved using the word-proximity search instead of a Boolean search.

• Journal of Information Management
• /
• v.40 no.4
• /
• pp.1-22
• /
• 2009

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we define scientific as a set of 10 types of named entities and technical terminologies in a biomedical domain. in order to automatically extract these entities from scientific documents at once, we develop a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer, co-reference resolver and terminology extractor. Each module of the integrated system has been evaluated with various corpus as well as KEEC 2009. The system will be utilized for various information service areas such as information retrieval, question-answering(Q&A), document indexing, dictionary construction, and so on.

• Annual Conference on Human and Language Technology
• /
• 2003.10d
• /
• pp.246-251
• /
• 2003

한국어의 언어적 특성에 의해 빈번하게 등장하는 명사와 기능어의 나열은 기능어나 연결 구문의 잦은 생략현상에 의해 복합 명사의 출현을 발생시킨다. 따라서, 한국어 분석에서 복합 명사의 처리 방법은 매우 중요한 문제로 인식되었으며 활발한 연구가 진행되어 왔다. 복합 명사의 의미 결정은 복합 명사구 내 단위 명사간의 의미적인 수식 관계를 고려하여 머리어의 선택과 의미를 함께 결정할 필요가 있다. 본 논문에서는 정보 검색의 색인어 추출 방법에서 사용되는 복합 명사구 내의 서술성 명사 처리를 이용하여 복합 명사의 의미 결정을 인접 명사의 의미 공기 정보가 아닌 구문관계에 따른 의미 공기 정보를 사용하여 분석하는 방법을 제시한다. 복합 명사구 내에서 구문적인 관계는 명사구 내에 서술성 명사가 등장하는 경우 보-술 관계에 의한 격 결정 문제로 전환할 수 있다. 이러한 구문 구조는 명사 의미를 결정할 수 있는 추가적인 정보로 활용할 수 있으며, 이때 구문 구조 파악을 위해 구축된 의미 제약 조건을 활용하도록 한다. 구조 분석에서 사용되는 격틀 정보는 동사와 공기하는 명사의 구문 관계를 분석하기 위해 의미 정보를 제약조건으로 하여 구축된다. 이러한 의미 격틀 정보는 단문 내 명사들의 격 결정과 격을 채우는 명사 의미를 결정할 수 있는 정보로 활용된다. 본 논문에서는 현재 개발중인 한영 기계 번역 시스템 Tellus-KE의 단문 단위 대역어 선정을 위해 구축된 의미 대역패턴인 동사구 패턴을 사용한다. 동사구 패턴에 기술된 한국어의 단문 단위 의미 격 정보를 사용하는 경우, 격결정을 위해 사용되는 의미 제약 조건이 복합 명사의 중심어 선택과 의미 결정에 재활용 될 수 있으며, 병렬말뭉치에 의해 반자동으로 구축되는 의미 대역 패턴을 사용하여 데이터 구축의 어려움을 개선하고자 한다. 및 산출 과정에 즉각적으로 활용될 수 있을 것이다. 또한, 이러한 정보들은 현재 구축중인 세종 전자사전에도 직접 반영되고 있다.teness)은 언화행위가 성공적이라는 것이다.[J. Searle] (7) 수로 쓰인 것(상수)(象數)과 시로 쓰인 것(의리)(義理)이 하나인 것은 그 나타난 것과 나타나지 않은 것들 사이에 어떠한 들도 없음을 말한다. [(성중영)(成中英)] (8) 공통의 규범의 공통성 속에 규범적인 측면이 벌써 있다. 공통성에서 개인적이 아닌 공적인 규범으로의 전이는 규범, 가치, 규칙, 과정, 제도로의 전이라고 본다. [C. Morrison] (9) 우리의 언어사용에 신비적인 요소를 부인할 수가 없다. 넓은 의미의 발화의미(utterance meaning) 속에 신비적인 요소나 애정표시도 수용된다. 의미분석은 지금 한글을 연구하고, 그 결과에 의존하여서 우리의 실제의 생활에 사용하는 $\ulcorner$한국어사전$\lrcorner$ 등을 만드는 과정에서, 어떤 의미에서 실험되었다고 말할 수가 있는 언어과학의 연구의 결과에 의존하여서 수행되는 철학적인 작업이다. 여기에서는 하나의 철학적인 연구의 시작으로 받아들여지는 이 의미분석의 문제를 반성하여 본다.반인과 다르다는 것이 밝혀졌다. 이 결과가 옳다면 한국의 심성 어휘집은 어절 문맥에 따라서 어간이나 어근 또는 활용형 그 자체로 이루어져 있을 것이다.으며, 레드 클로버 + 혼파 초지가 건물수량과 사료가치를 높이는데 효과적이었다.\ell}$ 이었으며 , yeast extract 첨가(添加)하여 배양시(培養時)는 yeast extract 농도(濃度)가 증가(增加)함에 따라 단백질(蛋白質) 함량(含量)도 증가(增加)하였다. 7. CHS-13 균주(菌株)의 RNA 함량(

• The Journal of the Korea Contents Association
• /
• v.7 no.7
• /
• pp.50-59
• /
• 2007

This paper describes a question analysis and expansion based on semantics for on efficient information retrieval. Results of all information retrieval systems include many non-relevant documents because the index cannot naturally reflect the contents of documents and because queries used in information retrieval systems cannot represent enough information in user's question. To solve this problem, we analyze user's question semantically, determine the answer type, and extract semantic features. And then we expand user's question using them and syntactic structures which are used to represent the answer. Our similarity is to rank documents which include expanded queries in high position. Especially, we found that an efficient document retrieval is possible by a question analysis and expansion based on semantics on natural language questions which are comparatively short but fully expressing the information demand of users.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.31 no.2
• /
• pp.99-118
• /
• 2020

The purpose of this study is to suggest the faceted classification scheme as a new classification scheme for 1,136 records (1950-1979) collected from UNESCO which are related to Korea. After extracting 1,601 nouns from the titles and index terms of the 1,136 records, they were classified and categorized based on the temporarily set fundamental categories. Through repeated classification and categorization, the last category names were derived as facets. As a result, the faceted classification scheme for Korea related records are structured into 10 basic facets and 38 sub-facets.

• The Journal of Society for e-Business Studies
• /
• v.14 no.4
• /
• pp.1-17
• /
• 2009

E-Catalogs which describe products or services are one of the most important data for the electronic commerce. E-Catalogs are created, updated, and removed in order to keep up-to-date information in e-Catalog database. However, when the number of catalogs increases, information integrity is violated by the several reasons like catalog duplication and abnormal classification. Catalog search, duplication checking, and automatic classification are important functions to utilize e-Catalogs and keep the integrity of e-Catalog database. To implement these functions, probabilistic models that use statistics of index words extracted from e-Catalogs had been suggested and the feasibility of the methods had been shown in several papers. However, even though these functions are used together in the e-Catalog management system, there has not been enough consideration about how to share common data used for each function and how to effectively manage statistics of index words. In this paper, we suggest a method to implement these three functions by using simple SQL supported by relational database management system. In addition, we use materialized views to reduce the load for implementing an application that manages statistics of index words. This brings the efficiency of managing statistics of index words by putting database management systems optimize statistics updating. We showed that our method is feasible to implement three functions and effective to manage statistics of index words with empirical evaluation.

• Therapeutic Science for Rehabilitation
• /
• v.12 no.4
• /
• pp.39-51
• /
• 2023

Objective : This study seeks to unveil the intellectual framework of research surrounding children's occupations by utilizing social network analysis of keywords from studies focused on childhood. Methods : From August 2003 to August 2023, we analyzed 3,364 keywords extracted from 270 research articles in the Korean Citation Index with the keyword "Child and Occupation" using the NetMiner program. Results : Research on children's work has increased quantitatively over the past decade. Keywords exhibiting a high degree of centrality in the realm of child occupation research included Task (0.055), Group therapy (0.040), Working memory (0.037), Intervention (0.033), Performance (0.030), Language (0.026), Ability (0.026), Skill (0.024), and Program (0.023). Notably, the weighted terms in the Word Network included Evaluation-Tool (30), School-Student (15), and Activity-Participation (15). The primary keywords from each topic in topic modeling were Activity (0.295), Disability (0.604), Education (0.356), Skill (0.478), School (0.317), Function (0.462), Disorder (0.324), Language (0.310), Comprehension (0.412), and Training (0.511). Conclusion : This study describes the trends in the domestic field of pediatric occupational research. These efforts provided valuable insights into pediatric occupational therapy in South Korea.

• Journal of Advanced Navigation Technology
• /
• v.19 no.4
• /
• pp.338-344
• /
• 2015

The national competency standards (NCS) is a systematize and standardize for skills required to perform their job. The NCS has developed a learning module with materialization and standardize by competence unit element, which is the unit of specific job competency. The existing curriculum is material to gab analysis for use in education training with competence unit element. The existing gab analysis has evaluated subjectively by experts. The gab analysis by experts bring up a subject subjective decision, accuracy lack, temporal and spatial inefficiency by psychological factor. This paper is proposed automated evaluation model for problem resolve of subjective evaluation. This paper use index term extraction, term frequency-inverse document frequency for feature value extraction, cosine similarity algorithm for gab analysis between existing curriculum and competence unit element. This paper was presented similarity mapping table between existing curriculum and competence unit element. The evaluation model in this paper should be complemented by an improved algorithm from the structural characteristics and speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.75-77
• /
• 2021

A variety of search engines are currently in use. The search engine supports the retrieval of data required by users through three stages: crawling, index generation, and output of search results based on meta-tag information. However, a large number of documents obtained by searching for keywords are often unrelated or scarce. Because of these problems, it takes time and effort to grasp the content from the search results and classify the accuracy. The index of search engines is updated periodically, but the criteria for weighted values and update periods are different from one search engine to another. Therefore, this paper uses the LSTM model, which extracts the relationship between keywords entered by the user and documents instead of the existing search engine, and improves the relationship between keywords and documents by entering keywords that the user wants to find.