• English Language & Literature Teaching
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• v.7 no.1
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• pp.137-160
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• 2001

This paper is to suggest the way to improve EFL reading skill through semantic mapping by leveled group activities. Semantic mapping is a categorical structuring of information in graphic forms or diagrams. It can be used to activate and organize background knowledge on topics in classrooms. For small group activities, the class is divided into higher leveled groups and lower leveled groups of four members based on their grades. The teaching process has three stages: Pre-reading, while-reading, and post-reading. In the pre-reading stage, students discuss what they know about the topic. They map ideas with a brainstorming technique. In the while-reading stage, they read the text about the topic. While they are reading, they could ask some questions they might have and discuss the information in the text and categorize them with semantic mapping. In the post-reading stage, they discuss what they thought of the topic and add some information about the topic with semantic mapping. For the subjects of this study, third grade, middle school students were selected: 41 students for the experimental group and 35 students for the control group. The experimental period covered almost one semester from March to August, 2000. The results were as follows: 1) The students in the experimental group had higher scores in reading comprehension than those in the control group when semantic mapping was used; 2) The use of semantic mapping in reading comprehension was found to be much more effective in the higher leveled group than in the lower leveled group; 3) The results of questionnaires showed that many students became more interested and motivated in English, and semantic mapping helped them to participate positively in reading the English text. Thus, using semantic mapping by leveled group activities can be an effective alternative to traditional teaching methods for teachers who desire to improve reading skill in middle school students' English classes.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.119-133
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• 2011

With the advent of Web 2.0, the use of RESTful web services is expected to overtake that of the traditional SOAP-based web services. Recently, the growing number of RESTful web services available on the web raises the challenging issue of how to locate the desired web services. However, the existing keyword searching method is insufficient for the bad recall and the bad precision. In this paper, we propose a novel building semantic ontology method which employs both the clustering technique based on association rules and the semantic analysis technique based on patterns. From this method, we can generate ontologies automatically, reduce the burden of semantic annotations, and support more efficient web services search. We ran our experiments on the subset of 168 RESTful web services downloaded from the PregrammableWeb site. The experimental results show that our method achieves up to 35% improvement for recall performance, and up to 18% for precision performance compared to the existing keyword searching method.

• Journal of Information Technology Services
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• v.16 no.2
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• pp.111-127
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• 2017

Computing Performance, sensor, storage, memory, and network costs have been steadily declining, and IoT services have recently become more active. The Internet of Things is linked with Big Data to create new business, and public institutions and corporations are hurry to import Internet of things. As the importance of the Internet of things has increased, the number of devices supporting the IoT has rapidly increased. With the development of the Internet of Things, various types of Internet services are being developed. For this reason, there is an increasing demand for IoT service designers and developers for IoT service case automatic search technology. IoT service designers can avoid duplication with existing services through service case retrieval and developers can save cost and time by combining existing reusable service equipment. This paper proposes IoT-Lite ontology for IoT and Semantic Web service to solve the above-mentioned problems. The existing ontologies for IoT, despite its many advantages, are not widely used by developers because it has not overcome the relatively slow drawbacks of increasing complexity and searching for development. To complement this, this study uses the IoT-Lite ontology introduced by W3C as a model and a semantic web service for automatic system retrieval. 3D camera, GPS, and 9-axis sensor, and IoT-Lite designed by IoT-Lite technique are integrated with the semantic technique and implemented directly.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.89-97
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• 2011

In semantic web information system like ontology that access distributed information from network, efficient query processing requires an advanced caching mechanism to reduce the query response time. P2P network system have become an important infra structure in web environment. In P2P network system, when the query is initiated, reducing the demand of data transformation to source peer is important aspect of efficient query processing. Caching of query and query result takes a particular advantage by adding or removing a query term. Many of the answers may already be cached and can be delivered to the user right away. In web environment, semantic caching method has been proposed which manages the cache as a collection of semantic regions. In this paper, we propose the semantic caching technique in cluster environment of peers. Especially, using schema data filtering technique and schema similarity cache replacement method, we enhanced the query processing efficiency.

• ETRI Journal
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• v.36 no.3
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• Journal of the Korea Society for Simulation
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• v.25 no.1
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• pp.11-20
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• 2016

Recently in the field of defense Modeling and Simulation (M&S), Component-Based Development technology is widely applying to save the cost and increase the reusability of weapon system development. Related with this, researches for rapid reconfiguration and simulation of the component-standardized weapon system is actively carrying out. To rapidly reconfigure the new weapon system, complex and various functions of component information has to be effectively searched. So, it requires differentiated search technique unlike existing Keyword-based Search method. Semantic Search System provides semantically related information among the extensive information. In this research, metadata of weapon system components and their representative functional words are built as an ontology. And it provides an ontology-based semantic search system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.54-62
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• 2010

Semantic Web programming is such an immature area that it is yet based on API calls, and does not provide high productivity in compiler time and sufficient efficiency in runtime. To get over this limitation, some efforts have been devoted on dedicated programming languages for Semantic Web. In this paper, we introduce a sophisticated cashing technique to enhance the runtime efficiency of RDF (Resource Description Framework) processing programs with SPARQL queries. We use static program analysis on those programs to determine what to be cashed, so as to decrease the cash miss ratio. Our method is implemented on programs in 'Jey' language, which is one of the programming languages devised for RDF data processing.

• Journal of Environmental Impact Assessment
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• v.8 no.1
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• pp.61-70
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• 1999

The purpose of this study is to understand an appreciation and a perceptive structure of "keijeok" image by Semantic Differential Technique and Factor Analysis. The data used in this study was obtained by the questionnaire survey carried out in Pusan metropolitan city. 15 adjective pairs in the survey were evaluated by the Semantic Differential scales graded 7 ranges from 1(very good) to 7(very bad). A total of 452 samples were collected by the survey and analyzed for this study. The results are as follows; First, 15 variables comprehended to "keijeok" image were estimated as a positive conception(LT 4.0). What's more, residents may perceive "keijeok" image as intangible and aesthetic aspect such as "fresh", "pleasant", "clean". Second, the result of factor analysis shows that factor I which express the major conceptual meaning of "keijeok" image tends to have intangible or aesthetic adjective pairs rather than concrete, whereas factor II which has the weaker meaning compared with factor I may represent a functional aspect of "keijeok" image. It can explain that the perceptive structure of "keijeok" image may be largely influenced by subjective sense, then added or concreted with objective conception or environmental situation. The results can be considered as an important matter which should be reflected at the stage of environmental planning for people's amiable and desirable place.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.585-592
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• 2005

The Semantic Web is being studied as the next step in the evolution of the web. In the environment of the Semantic Web, the information must be understandable computers as well as a just human. So we use ontologies for describing the contents of the web resources. Among such ontologies, OWL is proposed as a recommendation by W3C. OWL data is represented as graph structure and the query is evaluated by traversing each node of the graph. In this paper, we propose the optimization technique based on signature to efficiently process the OWL data. Our approach minimizes traversing each node of the graph in query processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2718-2731
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• 2023

This paper presents a method of using syntax and shallow semantic analysis for Vietnamese question generation (QG). Specifically, our proposed technique concentrates on investigating both the syntactic and shallow semantic structure of each sentence. The main goal of our method is to generate questions from a single sentence. These generated questions are known as factoid questions which require short, fact-based answers. In general, syntax-based analysis is one of the most popular approaches within the QG field, but it requires linguistic expert knowledge as well as a deep understanding of syntax rules in the Vietnamese language. It is thus considered a high-cost and inefficient solution due to the requirement of significant human effort to achieve qualified syntax rules. To deal with this problem, we collected the syntax rules in Vietnamese from a Vietnamese language textbook. Moreover, we also used different natural language processing (NLP) techniques to analyze Vietnamese shallow syntax and semantics for the QG task. These techniques include: sentence segmentation, word segmentation, part of speech, chunking, dependency parsing, and named entity recognition. We used human evaluation to assess the credibility of our model, which means we manually generated questions from the corpus, and then compared them with the generated questions. The empirical evidence demonstrates that our proposed technique has significant performance, in which the generated questions are very similar to those which are created by humans.