• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.435-442
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• 2001

According to the current increase of the usefulness of information in Internet, several methods are proposed in which multimedia information may be efficiently managed and retrieved. The purpose of this paper is to propose the new grouping method by SRR(Semantic Reference Relation) and DRR(Direct Reference Relation). The important point of this method proposed in this paper is to group MDI(Multimedia Document Informations) as a cluster of this multimedia objects. According to the result of experimental simulation, which has been tested by by the 1,000 multimedia items in internet, this method has made more efficiently the service and grouping of MDI possible than any other methods do in internet.

• ETRI Journal
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• v.32 no.3
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• pp.482-485
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• 2010

Feature selection is very important for feature-based relation classification tasks. While most of the existing works on feature selection rely on linguistic information acquired using parsers, this letter proposes new features, including probabilistic and semantic relatedness features, to manifest the relatedness between patterns and certain relation types in an explicit way. The impact of each feature set is evaluated using both a chi-square estimator and a performance evaluation. The experiments show that the impact of relatedness features is superior to existing well-known linguistic features, and the contribution of relatedness features cannot be substituted using other normally used linguistic feature sets.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.145-170
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• 2011

The purpose of this paper is to suggest a pilot model of knowledge organizing system which reflects the knowledge structure of research papers, using a case analysis on the "Korean Research Memory" of the National Research Foundation of Korea. In this paper, knowledge structure of the research papers in humanities and social science is described and the function of the "Korean Research Memory" for scholarly sense-making is analysed. In order to suggest the pilot model of the knowledge organizing system, the study also analysed the relation between indexed keyword and knowledge structure of research papers in the Korean Research Memory. As a result, this paper suggests 24 axioms and 11 inference rules for an ontology based on semantic relation of the knowledge structure.

• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.135-141
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• 2019

The purpose of this study is to extract the key words from the list of research subjects related to 'hotel workers' published in recent 10 years(2009~2018) by using the language network analysis method and to confirm the relation between the key words. In this paper, we propose a semantic network analysis that can overcome limitations of longitudinal study, analyze the recent research trends, and widely use as a research model. The results of this study are as follows ; First, in analyzing major key words in the title of 'Hotel Employer' in recent 10 years, the major keyword of job satisfaction(40), special grade(26), organizational commitment(20), emotional labor(19), service(12), restaurant(10), and turnover intention(9). Second, we analyzed the relation of language network among major key words extracted from the study title of 'hotel workers'. Such a research process is expected to grasp the trends of research related to 'hotel workers' and give implications for the future direction of related research.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.409-414
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• 2013

With the increase of multimedia information such as images, researches have been realized on how to extract the high-level semantic information from low-level visual information, and a variety of techniques have been proposed to generate this information automatically. However, most of these technologies extract the semantic information between single images, it's difficult to extract semantic information when a combination of multiple objects within the image. In this paper, we extract the visual features of objects within the image and training images stored in the DB and the features of each object are defined by measuring the similarity. Using ontology reasoner, each object feature within images infers the semantic information by positional relation and associative relation. With this, it's possible to infer semantic information between objects within images, we proposed a method for inferring more complicated and a variety of high-level semantic information.

• Korean Linguistics
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• v.48
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• pp.223-268
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• 2010

The purpose of this paper is to build a semantic networks of the 'cooking class' verb (based on 'CoreNet' of KAIST). This proceedings needs to adjust the concept classification. Then sub-categories of [Cooking] and [Foodstuff] hierarchy of CoreNet was adjusted for the construction of verb semantic networks. For the building a semantic networks, each meaning of 'Cooking verbs' of Korean has to be analyzed. This paper focused on the Korean 'heating' verbs and 'non-heating'verbs. Case frame structure and argument information were inserted for the describing verb information. This paper use a Propege 3.3 as a tool for building "cooking verb" semantic networks. Each verb and noun was inserted into it's class, and connected by property relation marker 'HasThemeAs', 'IsMaterialOf'.

• Cross-Cultural Studies
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• v.36
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• pp.271-291
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• 2014

In this paper, we maintain that case makers '에e' and '에게ege', and '에서eseo' and '에게서egeseo' are not two separate morphemes but are simply allomorphs of the same morphemes respectively. When '에e' and '에게ege' are used as a dative marker, they show exactly the same semantic function and are in complementary distribution in relation to the semantic features of their preceding noun; that is, if the preceding noun is an animate noun, '에게ege' is used and '에e' is used if not. Also, '에게서egeseo' and '에서eseo' as ablative and locative case makers show exactly the same semantic function and show complementary distribution depending on whether the preceding noun is animate or non-animate. Therefore, we assume that these markers are semantically conditioned allomorphs.

• The Journal of the Korea Contents Association
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• v.4 no.2
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• pp.36-42
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• 2004

This paper describes the Korean Question answering system using the syntactic-relation information d verbs to overcome lack of reliable knowledge and linguistic resources. The syntactic-relation information consists d the original form d a verb, usual usage pattern, semantic category of each dependent noun, synonym verbs and passive verbs. We use the syntactic-relation information to parse sentences or phrases with usual usage pattern of the verb and semantic conditions of dependent components on the verb. We also use that information to parse answer candidate sentences, and find an answer from questioned case slot. Our experiments that usage of the syntactic-relation information of verbs to mm lack of reliable knowledge and linguistic resources can be utilized efficiently for the Korean question answering 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.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.63-76
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• 2012

We propose a Korean compound noun semantic tagging system using statistical compound noun decomposition and semantic relation information extracted from a lexical semantic network(U-WIN) and dictionary definitions. The system consists of three phases including compound noun decomposition, semantic constraint, and semantic tagging. In compound noun decomposition, best candidates are selected using noun location frequencies extracted from a Sejong corpus, and re-decomposes noun for semantic constraint and restores foreign nouns. The semantic constraints phase finds possible semantic combinations by using origin information in dictionary and Naive Bayes Classifier, in order to decrease the computation time and increase the accuracy of semantic tagging. The semantic tagging phase calculates the semantic similarity between decomposed nouns and decides the semantic tags. We have constructed 40,717 experimental compound nouns data set from Standard Korean Language Dictionary, which consists of more than 3 characters and is semantically tagged. From the experiments, the accuracy of compound noun decomposition is 99.26%, and the accuracy of semantic tagging is 95.38% respectively.