• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.596-605
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• 2005

Conventional XML query methods such as simple keyword match or structural query expansion are not sufficient to catch the underlying information in the documents. Moreover, these methods inefficiently try to query all the documents. This paper proposes document tittering and query expansion methods that are based on ontology. Using ontology, we construct a universal DTD that can filter off unnecessary documents. Then, query expansion method is developed through the analysis of concept hierarchy and association among concepts. The proposed methods are applied on variety of sample XML documents to test the effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1637-1646
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• 2009

Recently many researchers have been studying various methods for data integration. Among the integration methods that the researchers have studied, there are a method using metadata repository, and Topic Map which identifies the relationships between the data. This study suggests Meta-Topic Map to create Topic Map about search keyword by applying metadata and Topic Map, and the XMDR as a way to connect Meta-Topic Map with metadata in the legacy system. Considering the semantic relationship of user's keyword in the legacy system, the Meta-Topic Map provides the Topic Map format and generates the Topic Map about user's keyword. The XMDR performs structural integration through solving the problem of heterogeneity among metadata in the legacy system. The suggested svides isproves the interoperability among existing Relational Database constructed in the legacy system and the search efficiency and is efficient in expanding the system.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.41-56
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• 2005

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance. we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.1-11
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• 2021

The purpose of this study was to analyze the trends and characteristics of 'stress urinary incontinence' research through word frequency analysis, and their relationships were modeled using word embedding. Abstract data of 9,868 papers containing abstracts in PubMed's MEDLINE were extracted using a Python program. Then, through frequency analysis, 10 keywords were selected according to the high frequency. The similarity of words related to keywords was analyzed by Word2Vec machine learning algorithm. The locations and distances of words were visualized using the t-SNE technique, and the groups were classified and analyzed. The number of studies related to stress urinary incontinence has increased rapidly since the 1980s. The keywords used most frequently in the abstract of the paper were 'woman', 'urethra', and 'surgery'. Through Word2Vec modeling, words such as 'female', 'urge', and 'symptom' were among the words that showed the highest relevance to the keywords in the study on stress urinary incontinence. In addition, through the t-SNE technique, keywords and related words could be classified into three groups focusing on symptoms, anatomical characteristics, and surgical interventions of stress urinary incontinence. This study is the first to examine trends in stress urinary incontinence-related studies using the keyword frequency analysis and word embedding of the abstract. The results of this study can be used as a basis for future researchers to select the subject and direction of the research field related to stress urinary incontinence.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.123-141
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• 2021

As the dependence of technology on the economic structure increases, the importance of National Core Technology is increasing. However, due to the nature of the technology itself, it is difficult to determine the scope of the technology to be protected because the scope of the relation is abstract and information disclosure is limited due to the nature of the National Core Technology. To solve this problem, we propose the most appropriate literature type and method of analysis to distinguish important technologies related to National Core Technology. We conducted a pilot test to apply TF-IDF, and LDA topic modeling, two techniques of text mining analysis for big data analysis, to four types of literature (news, papers, reports, patents) collected with National Core Technology keywords in the field of Display industry. As a result, applying LDA theme modeling to patent data are highly relevant to National Core Technology. Important technologies related to the front and rear industries of displays, including OLEDs and microLEDs, were identified, and the results were visualized as networks to clarify the scope of important technologies associated with National Core Technology. Throughout this study, we have clarified the ambiguity of the scope of association of technologies and overcome the limited information disclosure characteristics of national core technologies.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.428-437
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• 2005

An ontology consists of a set and definition of concepts that represents the characteristics of a given domain and relationship between the elements. To reduce time-consuming and cost in building ontology, this paper proposes a semiautomatic method to build a domain ontology using the results of text analysis. To do this, we Propose a terminology processing method and use the extracted concepts and semantic relations between them to build ontology. An experiment domain is selected by the pharmacy field and the built ontology is applied to document retrieval. In order to represent usefulness for retrieving a document using the hierarchical relations in ontology, we compared a typical keyword based retrieval method with an ontology based retrieval method, which uses related information in an ontology for a related feedback. As a result, the latter shows the improvement of precision and recall by $4.97\%$ and $0.78\%$ respectively.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.229-238
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• 2020

Upcoming 4th industrial revolution era and the post-covid19 made procedure, contents, and the ways of education innovative changes. Thesis analyzed the changes of educational procedures of universities unsing Bigkinds of 'KPF', (which is Korea Press Foundation) and DataLab system of 'Naver'. The following three results were derived from relational analysis, monthly keyword trend, and related word analysis with 633 cases searched for the keyword of "university curriculum innovation, creativity, and convergence." Firstly, the frequency of relationship keyword analysis of recent 4 years(2016~2020) was ministry of education(190), industrial revolution(154), system(137), career(136), global(131), smart(97), and enrolled students(95) in order. Secondly, The frequency of keywords related to the related words was Human Resources Development (136), Industrial-Academic Cooperation (119), Education Ministry (86), Specialization (69), and LiNC (62), which showed the importance of supporting the government (Ministry of Education) and fostering human resources, industry-academic cooperation, LiNC, and characterization in order to foster human resources in universities. According to this study, the paradigm of education is the artificial intelligence environment of the fourth industrial revolution, which is meaningful in presenting the direction of specialization, industry-academic cooperation, smart, and globalization, and the future direction of education that fosters creative talent in the era of the fourth industrial revolution.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.28-48
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• 2019

This research is aimed at identifying the perception associated with urban regeneration and predicting policy implications of future directions by analyzing the trend of urban regeneration depicted in the mass media by utilizing SNA (Semantic-Network Analysis) techniques. As the number of articles has increased, it is noted through analysis that the interrelationships between social phenomena and issues have combined to form the meaning of urban regeneration. Overall, 'urban' and 'regeneration' keywords also appeared at different periods, with 'urban' closely related to 'regeneration' starting in 1970 when urbanization was becoming more prevalent. It was analyzed that the frequency of 'urban' appeared more frequently in the early 1990s, while the frequency of 'rural' decreased sharply. Until the 1990s, the slums and the recession that appeared as side effects of urban problem-solving policies were mostly concentrated in cities. Policy discussions were conducted with the goal of improving the physical environment of cities rather than concentrating on the surrounding rural areas. The distributions of the keywords 'development' and 'regeneration' have increased quantitatively since the 1970s, and urban polarization has exploded due to the development of the external growth of cities, mirroring the trend of accelerated environmental threats. In particular, the keywords for 'regeneration' emerged mainly related to environmental problems, which led to the need for urban regeneration, and environmentally and ecologically friendly development. The emergence of "urban," "regeneration" and "environment" as keywords having to do with urban regeneration grew in the 1990s. This suggests that urban regeneration is now linked to "environment", as that has become a social issue.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.495-504
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• 2008

Recently, there has been much work on supporting keyword search not only for text documents, but a]so for structured data such as relational data, XML data, and RDF data. In this paper, we propose an efficient keyword search method for RDF data. The proposed method first groups related nodes and edges in RDF data graphs to reduce data sizes for efficient keyword search and to allow relevant information to be returned together in the query answers. The proposed method also utilizes the semantics in RDF data to measure the relevancy of nodes and edges with respect to keywords for search result ranking. The experimental results based on real RDF data show that the proposed method reduces RDF data about in half and is at most 5 times faster than the previous methods.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.119-133
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• 2011

With the advent of Web 2.0, the use of RESTful web services is expected to overtake that of the traditional SOAP-based web services. Recently, the growing number of RESTful web services available on the web raises the challenging issue of how to locate the desired web services. However, the existing keyword searching method is insufficient for the bad recall and the bad precision. In this paper, we propose a novel building semantic ontology method which employs both the clustering technique based on association rules and the semantic analysis technique based on patterns. From this method, we can generate ontologies automatically, reduce the burden of semantic annotations, and support more efficient web services search. We ran our experiments on the subset of 168 RESTful web services downloaded from the PregrammableWeb site. The experimental results show that our method achieves up to 35% improvement for recall performance, and up to 18% for precision performance compared to the existing keyword searching method.