• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.113-124
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• 2022

News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.

• Journal of the Korea Society for Simulation
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• v.16 no.1
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• pp.11-19
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• 2007

Under ubiquitous environment, applications can gather and utilize various sensing information. There are many issues such as energy management, protocol standardization, independency on sensor fields, and security to be resolved for the complete ubiquitous computing. Especially, the independent information access in the sensor field is one of the most important issues to maximize the usability of sensors in various sensor fields. However, existing frameworks are not suitable for the ubiquitous computing environment because of data heterogeneity between data elements in sensor fields. Existing applications are dependent to sensor fields and sensors in the existing ubiquitous computing on environment is dependent to the application in the sensor field. In other word, an application can utilize just information from a specific sensor field. To overcome this restriction, many issues from a hardware or software view must be resolved. In this paper, we provide the design and implementation of the Metadata Registry-based framework (UbiMDR) of the Ubiquitous environment. This framework can provides the semantic interoperability among ubiquitous applications or various sensor fields. In addition, we describe comparison evaluation between conventional Ubiquitous computing framework and UbiMDR framework with data accuracy of interoperability.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.562-567
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• 2008

XML은 W3C 표준으로 채택된 이후로 많은 어플리케이션에서 데이터를 표현하는 방법으로 사용되고 있다. XML문서는 특정 어플리케이션에 종속적이기 때문에 XSLT를 이용하여 변환한 뒤 사용하게 된다. 그러나 변환에는 많은 노력, 시간과 비용이 소요되기 때문에 이를 자동으로 변환하는 시스템을 구축하는 것이 최선의 방법이다. 이를 위해서 XTGen이나 XSLT 스크립트 시스템이 기존에 제안되었지만 사용자가 엘리먼트간의 관계를 수동으로 처리하는 방식이거나 변환 문서간 단말 노드의 1:1 매칭이라는 제약과 대규모 변환에 어려움이 있다. 본 논문은 JAWS를 이용한 엘리먼트간의 의미 관계 분석과 DTD의 구조를 분석하여 XSLT를 생성함으로써 기존 시스템들의 단점을 보완하고 더 높은 정확성을 보장한다는 장점을 가지고 있다. 본 논문에서 제안하는 시스템은 XML 문서를 변환하기 위한 XSLT를 자동으로 생성하여 XML 문서를 변환하는 모든 과정을 자동화 함으로써 문서 변환에 따르는 비용의 절감할 수 있을 것으로 기대된다.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.67-79
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• 2007

As the Web technologies and wired/wireless network are improved and various new multimedia services are introduced recently, need for searching multimedia including video data has been much increasing, The previous approaches for multimedia retrieval, however, do not make use of the relationships among semantic concepts contained in multimedia contents in an efficient way and provide only restricted search results, This paper proposes a multimedia retrieval system exploiting semantic relevancy of multimedia contents based on a domain ontology, We show the effectiveness of the proposed system by experiments on a prototype system we have developed. The proposed multimedia retrieval system can extend a given search keyword based on the relationships among the semantic concepts in the ontology and can find a wide range of multimedia contents having semantic relevancy to the input keyword. It also presents the results categorized by the semantic meaning and relevancy to the keyword derived from the ontology. Independency of domain ontology with respect to metadata on the multimedia contents is preserved in the proposed system architecture.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.727-731
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• 2010

Recently, mobile life-log data are collected by mobile devices and used to recode one's life. In order to help a user search data, a mobile life-log semantic network is introduced for storing logs and retrieving associative information. However, associative search systems on common semantic networks in previous studies provide for a user with only found data as text to users. This paper proposes an associative search system for mobile life-log semantic network that supports selection and keyword associative search of which a process and result are a visualized graph representing associative data and their relationships when a user inputs a keyword for search. In addition, by using semantic abstraction, the system improves user's understanding of search result and simplifies the resulting graph. The system's usability was tested by an experiment comparing the system and a text-based search system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1807-1824
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• 2015

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2632-2649
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• 2012

Most approaches to human action recognition is limited due to the use of simple action datasets under controlled environments or focus on excessively localized features without sufficiently exploring the spatio-temporal information. This paper proposed a framework for recognizing realistic human actions. Specifically, a new action representation is proposed based on computing a rich set of descriptors from keypoint trajectories. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors by the movement of the camera which is detected by the distribution of spatio-temporal interest points in the clips. A new topic model called Markov Semantic Model is proposed for semantic feature selection which relies on the different kinds of dependencies between words produced by "syntactic " and "semantic" constraints. The informative features are selected collaboratively based on the different types of dependencies between words produced by short range and long range constraints. Building on the nonlinear SVMs, we validate this proposed hierarchical framework on several realistic action datasets.

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.286-292
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• 2005

Continual attempt to accumulate and apply information eventually gives birth to the concept of the 'Semantic Web'. Thus, the 'Semantic Web' can be defined as a product of mankind's desire to standardize information. A term of knowledge is used as information or data in computer science. These are regarded and are divided sometimes each other in terminologies that have similar meaning. If it is divided, knowledge is different from information. However, some kind of information in Knowledge Representation is called knowledge often if it can be expressed in computing system. Therefore, knowledge representation can talk as information representation. The purpose of the study is systematizing knowledge through knowledge representation that uses Delphi technique and ontology is designed by utilizing assistance editor called protege-2000 to construct semantic web environment's ontology. Level of interest regarding the construction and evaluation of search systems based on ontology is set to increase. If defined well, semantic can reflect human's thinking to knowledge information on web. Furthermore systematizes knowledge, search of information and comprehension about Jeju tour using present computer may be done intelligence.