• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1310-1323
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• 2008

Recently, many studies on automated generation of composite Web services have been done. Most of these works compose Web services by chaining their inputs and outputs, but do not consider the functional semantics. Therefore, they may construct unsatisfied composite services against users' intention. Futhermore, they have high time-complexity since every possible combinations of available services should be considered. To resolve these problems, this paper proposes a sophisticated composition method that explicitly specifies and uses the functional semantics of Web services. Specifically, A graph model is constructed to represent the functional semantics of Web services as well as the dependency among inputs and outputs. On the graph, we search core services which provide the requested function ality and additional services which transform between I/O types of the user request and the core services. Then, composite services are built from combinations of the discovered services. The proposed method improves the semantic correctness of composite services by the functional semantics of Web services, and reduces the time complexity by combinations of functionally related services.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.31-38
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• 2009

Most 3D models found in the literature focus on theoretical topology for exterior 3D volumes. Although there are a few indoor models such as CityGML or IFC, implementing a full topology for the indoor spaces is either less practical due to the complexity or not even necessary in some application domains. Moreover, current spatial DBMSs do not support functionalities explicitly for 3D topological relations. In this study, an alternative method to build a 3D indoor model with less complexity ernativespatial DBMS is suggested. Focusnation the fact that semantic attributes can be storedion the floor surface, we suggestivemulti-layered 3D model for indoor spaces. We show the process to build the proposed model in the PostGIS, a spatial DBMS. And, then, as an example application, we illustrate the process to build and run a campus building information system.

• Annual Conference on Human and Language Technology
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• 1991.10a
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• pp.44-57
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• 1991

In the transfer machine translation, transfer dictionary decides the complexity of the transfer phase and the quality of translation according to the types and precision of informations supplied in the dictionary. Using the phrasal level translated informations within the human readable dictionary, human being translates a source sentence correctly and naturally. In this paper, we propose the verb transfer dictionary in which the various informations are constructed so the machine readable format that the Korean-to-English machine translation system can utilize them. In the proposed dictionary, we first provide the criterions by which an appropriate target verb is selected in phrase-to-phrase translations without an additional semantic analysis in transfer phase. Second, we provide the concrete sentence structure of a target verb so that we can resolve the expressive gaps between two languages and reduce the complexity of the various structure transfer in word-to-word translation.

• Journal of the Korean Institute of Landscape Architecture
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• v.22 no.2
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• pp.81-103
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• 1994

In recent years, the visual characteristics of natural open space and greenbelt surrounding the urban landscapes have been changed with sprawling of residential areas and highrised residential buildings. Since these natural areas being the background element of residential areas are topographically sloped mountains in many cities. It is easy to be seen in the distance and it is important to preserve these areas as a visual infrastructure of the urban landscape. The purposes of this study are to extract the factors of landscape impact evaluation for these areas and to clarify the physical landscape variables representing these factors, and to infer the visual-perceptional relationships between image and landscape variables. As results, conceptional three factors were extracted with semantic differential evaluation to classified 18 landscape slide, and three regression models were established with factor score of landscapes and physical variables measured in photographs. On the basis of these relationships, visual-perceptional characteristics were discussed by analyzing the data form eye-movement recording to each of landscapes. The factors of "spatial unfolding of backdropped hilly greenspace", "horizontal quence of residential buildings", and "landscape complexity" prove to be important. And it prove important variables of "skyline of mountainous ridge" and "visual edge of building structure" in regression models and eye fixation movements.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1150-1155
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• 2009

In the information society, the amount of information have been increased by technological development. It is not easy to deal with information for fast data processing because of increasing of the complexity and diversity of data. So this paper will confirm the fact that the color plays the role of the classification of complex information and can make data processing fast. Experiment 1 shows that the searching time of target(line name) is more faster when the color of a subway line is equal to the color of station`s name. Experiment 2 using the task for classification of word mixed in various categories shows that color category processing is more faster rather than semantic category processing and the effect of this task is far better when color difference is more clear.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.13-34
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• 1999

Building a knowledge base effectively has been an important research area in the expert systems field. A variety of approaches have been studied including rules, semantic networks, and frames to represent the knowledge base for expert systems. As the size and complexity of the knowledge base get larger and more complicated, the integration of knowledge based with database technology cecomes more important to process the large amount of data. However, relational database management systems show many limitations in handing the complicated human knowledge due to its simple two dimensional table structure. In this paper, we propose Object-Oriented Knowledge Store (OOKS), a knowledge base model on the basis of a frame sturcture using an object-oriented database. In the proposed model, managing rules for inferencing and facts about objects in one uniform structure, knowledge and data can be tightly coupled and the performance of reasoning can be improved. For building a knowledge base, a knowledge script file representing rules and facts is used and the script file is transferred into a frame structure in database systems. Specifically, designing a frame structure in the database model as it is, it can facilitate management and utilization of knowledge in expert systems. To test the appropriateness of the proposed knowledge base model, a prototype system has been developed using a commercial ODBMS called ObjectStore and C++ programming language.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.193-206
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• 2012

Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stake-holders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses.

• Journal of KIBIM
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• v.5 no.3
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• pp.41-50
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• 2015

Increasing size and complexity of indoor structures have led to much more complication in the spatial cognition and situational awareness. Contrary to outdoor environments, occupants have limited information regarding the indoor space syntax in terms of architectural and semantic information as well as how they interact with their surroundings. The availability of such information could give conveniences to both users and managers in various aspects. In order to visualize the exact location of rooms and utilities in 3D, many studies and projects have utilized BIM models because of its promising value of representing building components. In fact, the application of BIM provides definitive spatial indoor data and creates services for indoor space management and navigation. Therefore, this paper aims to provide an overview of practices on BIM-based indoor spatial information implementation and location-based services. It is expected that enabling of technologies, data-rich content and accessibility of information products will accelerate the growth of the spatially-related markets in various fields.