• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.375-378
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• 2016

The difficulty of "information sharing", "information reusing" issues happening in Wireless Sensor Network is due to the heterogeneity of the application environment, data processing, communication protocol etc. Based on the introduction of the Ontology theory, though analyzing the sensor characteristic a general type of sensor ontology contains the definition of concept, frame structure and OWL design was proposed from the standpoint of sensor observation. The paper expounded a system framework of the domain ontology through the expansion of knowledge base on the general sensor could achieve the information sharing and reuse by semantic communication between the general sensor ontology and user. The research of this method would bring new idea to the semantic sensor network construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.84-97
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• 2012

M2M (Machine-to-Machine) Technology makes it possible to network all kinds of terminal devices and their corresponding enterprise applications. Therefore, several M2M platforms were developed in China in order to collect information from terminal devices dispersed all over the local places through 3G wireless network. However, when enterprise applications try to integrate with M2M platforms, they should be maintained and refactored to adapt the heterogeneous features and properties of M2M platforms. Moreover, syntactical and semantic unification for information sharing among applications and devices are still unsolved because of raw data transmission and the usage of distinguished business vocabularies. In this paper, we propose and develop an M2M Middleware to support agile application integration with M2M platform. This middleware imports the event engine and XML-based syntax to handle the syntactical unification, makes use of Ontology-based semantic mapping to solve the semantic unification and adopts WebService and ETL techniques to sustain multi-pattern interactive approach, in order to agilely make applications integrated with the M2M platform. Now, the M2M Middleware has been applied in the China Telecom M2M platform. The operation results show that applications will cost less time and workload when being integrated with M2M platform.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.175-186
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• 2022

This research article develops an Arabic books' recommendation system, which is based on the content similarity that assists users to search for the right book and predict the appropriate and suitable books pertaining to their literary style. In fact, the system directs its users toward books, which can meet their needs from a large dataset of Information. Further, this system makes its predictions based on a set of data that is gathered from different books and converts it to vectors by using the TF-IDF system. After that, the recommendation algorithms such as the cosine similarity, the sequence matcher similarity, and the semantic similarity aggregate data to produce an efficient and effective recommendation. This approach is advantageous in recommending previously unrated books to users with unique interests. It is found to be proven from the obtained results that the results of the cosine similarity of the full content of books, the results of the sequence matcher similarity of Arabic titles of the books, and the results of the semantic similarity of English titles of the books are the best obtained results, and extremely close to the average of the result related to the human assigned/annotated similarity. Flask web application is developed with a simple interface to show the recommended Arabic books by using cosine similarity, sequence matcher similarity, and semantic similarity algorithms with all experiments that are conducted.

• 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 Gifted/Talented Education
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• v.25 no.5
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• pp.673-696
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• 2015

Students' perception of science-related career strongly influences the formation of career motivation in science. Especially, the high level of science gifted students' positive perceptions plays an important role in allowing them to continue to study science. This study compared perceptions of science-related career between general and gifted middle school students using semantic network analysis. To ensure this end, we first structuralize semantic networks of science-related careers that students perceived. Then, we identified the characters of networks that two different student groups showed based on the structure matrix indices of semantic network analysis. The findings illustrated that the number of science-related careers shown in science gifted students' answer is more than in general students' answer. In addition, the science gifted students perceived more diverse science-related careers than general students. Second, scientific career such as natural scientists and professors were shown in the core of science gifted students' perception network whereas non-research oriented careers such as science teachers and doctors were shown in the core of general students' perception network. In this study, we identified the science gifted students' perceptions of science-related career was significantly different from the general students'. The findings of current study can be used for the science teachers to advise science gifted students on science-related careers.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Language and Information
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• v.14 no.1
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• pp.165-196
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• 2010

The primary goal of this paper is to propose methods of enriching two heterogeneous ontologies: Sejong Semantic Classes (SJSC) and KorLexNoun 1.5 (KLN). In order to achieve this goal, this study introduces the pros and cons of two ontologies, and analyzes the error patterns found during the fine-grained manual mapping processes between them. Error patterns can be classified into four types: (1) structural defectives involved in node branching, (2) errors in assigning the semantic classes, (3) deficiency in providing linguistic information, and (4) lack of the lexical units representing specific concepts. According to these error patterns, we propose different solutions in order to correct the node branching defectives and the semantic class assignment, to complement the deficiency of linguistic information, and to increase the number of lexical units suitably allotted to their corresponding concepts. Using the results of this study, we can obtain more enriched ontologies by correcting the defects and errors in each ontology, which will lead to the enhancement of practicality for syntactic and semantic analysis.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.167-176
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• 2018

In this study, Big Data was used to collect the information related to 'Cooking Academy' keywords. After collecting all the data, we calculated the frequency through the text mining and selected the main words for future data analysis. Data collection was conducted from Google Web and News during the period from January 1, 2013 to December 31, 2017. The selected 64 words were analyzed by using UCINET 6.0 program, and the analysis results were visualized with NetDraw in order to present the relationship of main words. As a result, it was found that the most important goal for the students from cooking school is to work as a cook, likewise to have practical classes. In addition, we obtained the result that SNS marketing system that the social sites, such as Facebook, Twitter, and Instagram are actively utilized as a marketing strategy of the institute. Therefore, the results can be helpful in searching for the method of utilizing big data and can bring brand-new ideas for the follow-up studies. In practical terms, it will be remarkable material about the future marketing directions and various programs that are improved by the detailed curriculums through semantic network of cooking school by using big data.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.118-141
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• 2019

In South Korea, as awareness of gender equality increased since the 1990s, policies for gender equality and social awareness of equality have been established. Until recently, however, the gap between men and women in social and economic activities has not reached the globally desired level and led to social conflict throughout the country. In this study, we analyze the content of online news comments to understand the public perception of gender equality and the details of gender conflict and to grasp the emergence and diffusion process of emerging issues on gender equality. We collected text data from the online news that included the word 'gender equality' posted from January 2012 to June 2017 and also collected comments on each selected news item. Through text mining and the temporal semantic network analysis, we tracked the changes in discourse on gender equality and conflict. Results revealed that gender conflicts are increasing in the online media, and the focus of conflict is shifting from 'position and role inequality' to 'opportunity inequality'.