• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.101-121
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• 2018

This study proposes the analysis method in sentence semantics that can be automatically identified and processed as appropriate items in the system according to the composition of the sentences contained in the data corresponding to the logical semantic structure metadata of the research papers. In order to achieve the purpose, the structure of sentences corresponding to 'Research Objectives' and 'Research Outcomes' among the semantic structure metadata was analyzed based on the number of words, the link word types, the role of many-appeared words in sentences, and the end types of a word. As a result of this study, the number of words in the sentences was 38 in 'Research Objectives' and 212 in 'Research Outcomes'. The link word types in 'Research Objectives' were occurred in the order such as Causality, Sequence, Equivalence, In-other-word/Summary relation, and the link word types in 'Research Outcomes' were appeared in the order such as Causality, Equivalence, Sequence, In-other-word/Summary relation. Analysis target words like '역할(Role)', '요인(Factor)' and '관계(Relation)' played a similar role in both purpose and result part, but the role of '연구(Study)' was little different. Finally, the verb endings in sentences were appeared many times such as '~고자', '~였다' in 'Research Objectives', and '~었다', '~있다', '~였다' in 'Research Outcomes'. This study is significant as a fundamental research that can be utilized to automatically identify and input the metadata element reflecting the common logical semantics of research papers in order to support researchers' scholarly sensemaking.

• Journal of Korean Library and Information Science Society
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• v.47 no.2
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• pp.51-70
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• 2016

The purpose of this study is to analyze the semantic relation network and valence-arousal dimension through the words that describe emotions in Korean language. The results of this analysis are summarized as follows. Firstly, each emotion word was semantically linked in the network. This particular feature hinders differentiating various types of "emotion words" in accordance with similarity in meaning. Instead, central emotion words playing a central role in a network was identified. Secondly, many words are classified as two categories at the valence and arousal level: (1) negative of valence and high of arousal, (2) negative of valence and middle of arousal. This aspects of Korean emotional words would be useful to analyze emotions in various text data of books and document information.

• Journal of the Korean Society for information Management
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• v.26 no.4
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• pp.93-112
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• 2009

Recently in the area of the information environment, there are lively discussions about search 2.0 which is representative of the next generation search services. In this study, we divide information search model into matching and linking models according the developmental stages. Therefore, on the one hand, we analyze the background, main concepts, related attributes and cases of the next generation search services and the other, we identify the representative keywords by the group analysis of various attributes and cases of it. The result shows that the main keywords such as social search, artificial intelligence and semantic search, and relation/network based search are representative of the search 2.0.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.17 no.1
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• pp.285-303
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• 2006

This study suggests the method of converting thesauri to SKOS step by step and it is formalized in three stages of the conversion process. The study develops output and guidelines for each stage. The converting stages are: (1) Collecting and analyzing thesauri for understanding about structure of terms and semantics of relation. (2) Defining the conversion method and creating ontology of the thesauri. (3) Examining the preservation of forms and various semantic relations between the thesauri and then creating SKOS ontology. This method can be applied to the thesauruses with complicated relations in concepts. In the future, it is needed to have an embodiment of conversion after making the algorithm of conversion by stage with the method suggested in this research.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.93-105
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• 2013

Much Interest has focused on social network services such as Facebook and Twitter. Previous research conducted on social network often emphasized the architecture of the social network that is the existence of path between any objects on network and the centrality of the object in the network. However, studies on the semantic association in the network are rare. Studies on searching semantic associations between entities are necessary for future business enhancements. In this research, the ontology based social network analysis is performed. A new method to search and rank relation sequences that consist of several relations between entities is proposed. In addition, several heuristics to measure the strength of the relation sequences are proposed. To evaluate the proposed method, an experiment was performed. A group of social relationships among the university and organizations are constructed. Some social connections are searched using the proposed ranking method. The proposed method is expected to be used to search the association among entities in ontology based knowledge base.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1252-1261
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• 2009

One notable challenge in video searching and summarizing is extracting semantic from video contents and annotating context for video contents. Video semantic or context could be obtained by two methods to extract objects and contexts between objects from video. However, the method that use just to extracts objects do not express enough semantic for shot or scene as it does not describe relation and interaction between objects. To be more effective, after extracting some objects, context like relation and interaction between objects needs to be extracted from conversation situation. This paper is a study for how to detect speaker and how to compose context for talking to annotate conversation context. For this, based on this study, we proposed the methods that characters are recognized through face recognition technology, speaker is detected through mouth motion, conversation context is extracted using the rule that is composed of speaker existing, the number of characters and subtitles existing and, finally, scene context is changed to xml file and saved.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• Journal of KIISE:Information Networking
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• v.32 no.2
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• pp.123-133
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• 2005

In recent years, service discovery is one of the major technologies of home networks which head for a pervasive computing environment. However, existing service discovery techniques are difficult to understand semantics, and they only provide syntactic level service matching. To solve these problems, we have designed and developed ontology for semantic service discovery. Our ontology could enrich the amount of devices and services representations with semantics, and the relation of devices and service could be efficiently described through primitive service. For representing context information of devices, we describe attributes of device including location information, device status and etc. To determine whether the developed ontology can be applied to service discovery systems, we have implemented a semantic service discovery system by extension of the existing Jini lookup service. Also, we have evaluated our ontology with associated software environment according to some experiment scenarios, and have proved the usefulness of our ontology-based semantic service discovery system.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.1-9
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• 2009

Many techniques have been proposed to store efficiently and query XML data. One way achieving this goal is using relational database by transforming XML data into relational format. But most researches only transformed content and structure of XML schema. Although they transformed semantic constrainment of XML schema, they did not all of semantics. In this paper, we propose a systematic technique for extracting semantic constrainment from XML schema and storing method when the extracting result is transformed into relational schema without any lost of semantic constrainment. The transforming algorithm is used for extracting and storing semantic constrainment from XML schema and it shows how extracted information is stored according to schema notation. Also it provides semantic knowledges that are needed to be confirmed during the transformation to ensure a correct relation schema. The technique can reduce storage redundancy and can keep up content and structure with integrity constraints.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.3
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• pp.560-568
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• 2001

The aim of the present study was to measure intra-individual consistency in clothing and fabric evaluation and to examine its relation to the ratings. A sample of 93 female and 97 male university students rated clothing of 4 styles of daytime wear and 2 fabrics on 15 pairs of polar adjectives twice in 7-days interval. Correlation coefficients between the two ratings for each subject, intra-individual consistency in the evaluation, ranged from -0.12 to 0.89 and mean coefficient was 0.63 of female and -0.01 to 0.78 and mean coefficient was 0.54 of male. Based on the coefficients, the subjects were classified into three groups: high, medium, and low intra-individual consistency. Analysis of variance of mean ratings by the three groups revealed that significant difference existed in 24% of female and 23% of male in 90 combinations of 6 clothing and 15 semantic differential scales. Female of subjects with high intra-individual consistency were most likely definite to evaluate clothing, whereas the ones with low were least. But male subjects were not definite. Mean correlation coefficients for style evaluation subscales of female was 0.39, but male was 0.44. Among the semantic differential scales, high stability in the two ratings was observed for the synthetic clothing evaluation. Correlation coefficients for each clothing obtained from the mean score of the subjects in each semantics differential scale were around 0.98, including that the mean scores of the subjects in each scale could yield excellent stability in clothing evaluation.