• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1080-1097
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• 2010

Well organized and easy usable Grid management system is very important for executing various Grid applications and managing Grid computing environment. Moreover, information system which can support Grid management system by providing various Grid environment related information is also one of the most interesting issue in the Grid middleware system area. Effective cooperation between Grid management system and information system can make a novel Grid middleware system. Especially, service oriented architecture based Grid management system is flexible and extensible for providing various type of Grid services. Also, information system based on data mining process which comprises various different kinds of domains such as users, resources and applications can make Grid management system more precise and efficient. In this paper, we propose semantic Grid middleware system which is a combination of Grid management system and semantic information system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.59-72
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• 2013

In this paper, we propose a keyword query processing technique based on semantic relationships for OWL data. The proposed keyword query processing technique can improve user's search satisfaction by performing two types of associative search. The first associative search uses information inferred by the relationships between classes or properties during keyword query processing. And it supports to search all information resources that are either directly or indirectly related with query keywords by semantic relationships between information resources. The second associative search returns not only information resources related with query keywords but also values of properties of them. We design a storage schema and index structures to support the proposed technique. And we propose evaluation functions to rank retrieved information resources according to three criteria. Finally, we evaluate the validity and accuracy of the proposed technique through experiments. The proposed technique can be utilized in a variety of fields, such as paper retrieval and multimedia retrieval.

• Language and Information
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• v.9 no.1
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• pp.19-34
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• 2005

Ontology is an indispensable component in intelligent and semantic processing of knowledge and information, such as in semantic web. However, ontology construction requires vast amount of data collection and arduous efforts in processing these un-structured data. This study proposed a methodology to automatically construct and generate ontologies from Sejong Electronic Dictionary. As Sejong Electronic Dictionary is structured in XML format, it can be processed automatically by computer programmed tools into an OWL(Web Ontology Language)-based ontologies as specified in W3C . This paper presents the process and concrete application of this methodology.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.199-203
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• 2023

In recent decades, the growth of communication technology has resulted in an explosion of data-related information. Ontology perception is being used as a growing requirement to integrate data and unique functionalities. Ontologies are not only critical for transforming the traditional web into the semantic web but also for the development of intelligent applications that use semantic enrichment and machine learning to transform data into smart data. To address these unclear facts, several researchers have been focused on expanding ontologies and semantic web technologies. Due to the lack of clear-cut limitations, ontologies would not suffice to deliver uncertain information among domain ideas, conceptual formalism supplied by traditional. To deal with this ambiguity, it is suggested that fuzzy ontologies should be used. It employs Ontology to introduce fuzzy logical policies for ambiguous area concepts such as darkness, heat, thickness, creaminess, and so on in a device-readable and compatible format. This survey efforts to provide a brief and conveniently understandable study of the research directions taken in the domain of ontology to deal with fuzzy information; reconcile various definitions observed in scientific literature, and identify some of the domain's future research-challenging scenarios. This work is hoping that this evaluation can be treasured by fuzzy ontology scholars. This paper concludes by the way of reviewing present research and stating research gaps for buddy researchers.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.125-136
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• 2007

The study on query relaxation which provides approximate answers has received attention. In recent years, some arguments have been made that semantic relationships are useful to present the relationships among data values and calculating the semantic distance between two data values can be used as a quantitative measure to express relative distance. The aim of this article is a hierarchical metricized knowledge abstraction (HiMKA) with an emphasis on combining data abstraction hierarchy and distance measure among data values. We propose the operations and the query relaxation algorithm appropriate to the HiMKA. With various experiments and comparison with other method, we show that the HiMKA is very useful for the quantified approximate query answering and our result is to offer a new methodological framework for query relaxation.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.33-43
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• 2024

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.