• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.273-280
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• 2021

In the past, the government made a total of 357 hydrophilic districts into parks to create rest areas in the national river with the four major river projects. According to the results of the survey, 60 water-friendly districts with low utilization were lifted in January 2017, and 297 water-friendly districts are currently being managed. Local governments are in charge of the maintenance costs necessary to maintain these hydrophilic districts, which require considerable costs, so it is necessary to accurately grasp the characteristics and needs of local residents at the operation stage after designation. In this study, the characteristics of local residents in the hydrophilic district were analyzed by correlating social factors with river users, crawling social network data to analyze visit patterns, and derived related Keywords, and analyzed the characteristics of the hydrophilic district. The study target areas are Samrak and Daejeo Ecological Park, located downstream of the Nakdonggang River. Social factors analyzed real estate transaction price data, economic activity income, households, stress perception rate, and pet breeding status through public data provided by Statistics Korea, and analyzed user visit patterns and image keywords on weekends.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.35-45
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• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.

• Korean Journal of Construction Engineering and Management
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• v.21 no.2
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• pp.15-21
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• 2020

The relationship between keywords, found in all productivity related papers published in the KSCE journal for last 15 years, were analyzed in order to reveal a research trend in the area using text mining and A-Priori algorithm. As the results, it is found that the word of 'productivity' is most closely related to the words of 'work' and 'labor'. Futhermore, the word is somewhat related to those of 'factor', 'model', simulation', and 'work time'. It is also revealed that, on the other hand, the words of 'machine' and 'equipment' have little relationships with the keyword. This research will be a great help for academia to understand a research trend in the area of construction productivity.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.525-528
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• 2007

기존의 웹서비스가 정적이고 수동적인데 반해 최근의 웹 서비스는 점차 동적이고 능동적으로 변화하고 있다. 이러한 웹서비스 변화의 흐름을 잘 반영하는 것이 웹 2.0이다. 웹 2.0에서 대부분의 정보는 사용자에 의해 생산되고, 사용자가 붙인 태그(tag)에 의해 분류되어진다. 그러나 현재 태그에 관한 서비스 및 연구들은 태깅(tagging) 방법에 대한 연구를 비롯해 이를 표현하기 위한 tag cloud에 초점이 맞춰져 진행됨에 따라, 다양한 태그 정보자원 간의 체계와 연결 관계인 지식체계를 제공하지 못하고 있다. 이에 본 논문에서는 체계화된 지식표현을 위해 웹상에 편재되어 있는 학습 관련 리소스(resources) 및 태그들를 수집한다. 이를 사용자가 요청한 검색 키워드와 연관성이 있는 태그 정보들을 맵핑 및 클러스터링하여 최적화된 표현 형식인 토픽 맵(topic map)화하기 위한 시스템을 제안하며, 이 중 토픽 맵 생성을 위한 초기 연구 단계로서, 연관 태그들 간의 맵핑 및 클러스터링을 위한 알고리즘 제시를 중심으로 소개한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.154-158
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• 2008

시간이 지날수록 처리해야 하는 정보가 점점 늘어나고 있어서 각 분야에서는 온톨로지를 구축하여 그것을 기반으로 보다 정확한 결과를 얻으려는 시도를 하고 있다. 한의학 분야에서도 온톨로지를 이용한 약재 정보 관리를 시도하고 있다. 하지만 한의학 약재 온톨로지에서는 약재 사이의 연관성 파악이 중요하지만, 기존의 검색은 키워드 기반의 검색으로 약재 사이의 연관성을 확인하게 어려움이 있다. 온톨로지의 구조적 내용을 파악하기 위한 기존의 온톨로지 뷰어들이 존재하지만 약재 온톨로지가 가지는 계층구조 위주의 탐색이 어렵고, 다양한 속성이 속성에 관계없이 그래프 상에 고르게 분포하기 때문에 속성에 따른 약재의 구분이 어렵다는 문제점이 존재한다. 따라서 기존의 뷰어를 수정 및 보완하여 한의학 약재 온톨로지에서 계층구조 파악 및 속성 별 약재 분류를 파악할 수 있는 뷰어를 개발하였다. 이러한 뷰어시스템을 통해 향후 한의학 전반적인 분야의 자원을 단계별로 체계화하여 관리함으로써 사용자 중심의 통합되고 현대화된 전통 의학 정보의 서비스의 기초시스템으로 활용될 수 있을 것이다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.3
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• pp.247-267
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• 2011

The purpose of this study is to investigate knowledge structure of knowledge organization research area in Korea. The study employed content analysis and network analysis to analyze degree centrality, betweenness, and eigenvector as well as frequency of words. It also analyzes research articles published during the period of 2000-2001. The study can be summarized that the network of keywords of knowledge organization researches is compact and complicated. Cataloging and classification play important roles in the network, and metadata and ontology becomes focal areas in knowledge organization. On the other hand, networks of authorships and authors are broad and fragmented. Collaboration is not active enough.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.37-53
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• 2005

The terminology management is absolutely necessary for maintaining the efficiency of thesaurus. This is because the creating, differentiating, disappearing, and other processes of the descriptor become accomplished dynamically, making effective management of thesaurus a very difficult task. Therefore, a device is required for accomplishing methods to construct and maintain the thesaurus. This study proposes the methods to construct the thesaurus management using the basic elements of a topic map which are topic, occurrence, and association. Second, the study proposes the methods to represent the basic and specific instances using the systematic mapping algorithm and merging algorithm. Also, using a hub document as a standard, this study gives the methods to expand and subsitute the descriptors using the topic type. The new method applying fixed concept for double layer management on terms is developed, too. The purpose of this method is to fix the conceptual term which represents independent concept of time and space, and to select the descriptor freely by external information circumstance.

• Journal of Food Hygiene and Safety
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• v.32 no.2
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• pp.96-100
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• 2017

Through the increasing use of internet and smart device, consumers can search the information what they want to find. The information has been accumulated and become into a big data. Analyzing the big data regarding key words associated with foods and foodborne pathogens could be a method for predicting foodborne illness outbreaks, especially in school food services. Therefore, the objective of this study was to elucidate the correlations between key words associated with foods and food safety issues. Frequencies of the key words for foodborne pathogens and food safety issues were searched using an internet portal site from January 1, 2012 to December 31, 2014. In addition, foodborne outbreak data were collected from Ministry of Food and Drug Safety for the same period of time. There was correlation between the time having maximum key word frequencies of foods and foodborne pathogens, and the time for foodborne illness outbreak occurred. In addition, the search frequencies for foods and foodborne pathogens were generally increased right after foodborne outbreaks occurred. However, in some cases foodborne outbreaks occurred after the search frequencies for certain seasonal foods increased These results could be useful in food safety management for reducing foodborne illness and in food safety communication.

• Journal of Technology Innovation
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• v.20 no.1
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• pp.199-234
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• 2012

Technology roadmaps (TRMs) are considered to be the essential tool for strategic technology planning and management. Recently, rapidly evolving technological trends and severe technological competition are making TRM more important than ever before. That is because TRM plays a role of "map" that align organizational objectives with their relevant technologies. However, constructing and managing TRMs are costly and time-consuming because they rely on the qualitative and intuitive knowledge of human experts. Therefore, enhancing the productivity of developing TRMs is one of the major concerns in technology planning. In this regard, this paper proposes a technology roadmapping approach based on function of which concept includes objectives, structures and effects of a technology and which are represented as Subject-Action-Object structures extractable by exploiting natural language processing of patent text. We expect that the proposed method will broaden experts' technological horizons in the technology planning process and will help to construct TRMs efficiently with the reduced time and costs.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.