• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.36-49
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• 2005

Recently, many researchers have given high attention to the semantic web services based on the semantic web technology While existing web services use the XML-based web service description language, WSDL, semantic web services are utilizing web service description languages such as DAML-S in ontology languages. The researchers of semantic web services are generally focused on web service discovery, web service invocation, web service selection and composition, and web service execution monitoring. Especially, the semantic web service discovery as the basis to accomplish the ultimate semantic web service environment has some different properties from previous information discovery areas. Hence, it is necessary to develop the storage system and discovery mechanism appropriate to the semantic well description languages. Even though some related systems have been developed, they are not appropriate for the embedded system environment, such as intelligent robotics, in which there are some limitations on memory disk space, and computing power In this regard, we in the embedded system environment have developed the document management system which efficiently manages the web service documents described by DAML-S for the purpose of the semantic web service discovery, In addition, we address the distinguishing characteristics of the system developed in this paper, compared with the related researches.

• Korean Journal of Cognitive Science
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• v.30 no.3
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• pp.133-156
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• 2019

To investigate how personality test items are understood by participants, their semantic representations were explored by Latent Semantic Analysis, In this thesis, Semantic Similarity Matrix was proposed, which contains cosine similarity of semantic representations between test items and personality traits. The matrix was compared to traditional factor loading matrix. In preliminary study, semantic space was constructed from the passages describing the five traits, collected from 154 undergraduate participants. In study 1, positive correlation was observed between the factor loading matrix of Korean shorten BFI and its semantic similarity matrix. In study 2, short personality test was constructed from semantic similarity matrix, and observed that its factor loading matrix was positively correlated with the semantic similarity matrix as well. In conclusion, the results implies that the factor structure of personality test can be inferred from semantic similarity between the items and factors.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.91-103
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• 2004

Multi-agent systems for semantic web applications require efficient implementation of agent architectures without sacrificing the flexibility and the level of abstraction that agent architectures provide. In this paper, we present an agent system, called VivAce, which is implemented in Java to achieve both high efficiency and the level of abstraction provided by the BDI agent architecture. VivAce (Vivid Agent Computing Environment) has the characteristics of a vivid agent through the BDI agent model. A vivid agent is a software-controlled system whose state comprises the mental components of knowledge, perceptions, tasks, and intentions, and whose behavior is represented by means of action and reaction rules. We first identify the requirements for multi-agent systems and then present the relevant features of VivAce and experimental results.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.117-122
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• 2018

As evidenced through rapidly growing digital repositories and web resources, automatic metadata generation is becoming ever more critical, especially considering the costly and complex operation of manual metadata creation. Also, automatic metadata generation is apt to consistent metadata application. In this sense, metadata quality and interoperability can be enhanced by utilizing a mechanism for automatic metadata generation. In this article, a mechanism of automatic metadata extraction called ExMETA is introduced in order to alleviate issues dealing with inconsistent metadata application and semantic interoperability across ever-growing digital collections. Conceptual graph, one of formal languages that represent the meanings of natural language sentences, is utilized for ExMETA as a mediation mechanism that enhances the metadata quality by disambiguating semantic ambiguities caused by isolation of a metadata element and its corresponding definition from the relevant context. Hence, automatic metadata generation by using ExMETA can be a good way of enhancing metadata quality and semantic interoperability.

• 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 Information Processing Systems
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• v.10 no.3
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• pp.335-354
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• 2014

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.

• Journal of Computing Science and Engineering
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• v.2 no.1
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• pp.44-73
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• 2008

There is general agreement that the problem of data semantics has to be addressed for XML data to become machine-processable. This problem can be tackled by defining a semantic mapping between an XML schema and an ontology. Unfortunately, creating such mappings is a tedious, time-consuming, and error-prone task. To alleviate this problem, we present a solution that heuristically discovers semantic mappings between XML schemas and ontologies. The solution takes as input an initial set of simple correspondences between element attributes in an XML schema and class attributes in an ontology, and then generates a set of mapping formulas. Once such a mapping is created, it is important and necessary to maintain the consistency of the mapping when the associated XML schema and ontology evolve. In this paper, we first offer a mapping formalism to represent semantic mappings. Second, we present our heuristic mapping discovery algorithm. Third, we show through an empirical study that considerable effort can be saved when discovering complex mappings by using our prototype tool. Finally, we propose a mapping maintenance plan dealing with schema evolution. Our study provides a set of effective solutions for building sustainable semantic integration systems for XML data.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.1-13
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• 2004

The Semantic Web introduces the next generation of the Web by establishing a semantic layer of machine-understandable data to enable machines (i.e intelligent agents) retrieve more relevant information and execute automated web services using semantic information. Ontology-related technologies are very important to evolve the World Wide Web of today into the Semantic Web in representation and share of semantic data. In this paper, we proposed and implemented the efficient ontology management system, K-Box, which constructs and manages ontologies using topic maps. We can use K-Box system to construct, store and retrieve ontologies. K-Box system has several components: Topicmap Factory, Topicmap Provider, Topicmap Query Processor, Topicmap Object Wrapper, Topicmap Cache Manager, Topicmap Storage Wrapper.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.107-117
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• 2010

The feature models representing the common and variable concepts among the software products and the feature configurations generated by selecting the features to be included in the target product are the essential components in the software product lines methodology. Although the researches on the formal semantics and reasoning of the feature models and feature configurations are in progress, the researches on feature model ontologies and feature configuration validation using the semantic web technologies are yet insufficient. This paper defines the formal semantics of the feature models and proposes a feature configuration validation technique based on ontology and semantic web technologies. OWL(Web Ontology Language), a semantic web standard language, is used to represent the knowledge in the feature models and the feature configurations. SWRL(Semantic Web Rule Language), a semantic web rule languages, is used to define the rules to validate the feature configurations. The approach in this paper provides the formal semantic of the feature models, automates the validation of feature configurations, and enables the application of various semantic web technologies, such as SQWRL.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.51-55
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• 2014

의미역 결정 (Semantic Role Labeling) 은 문장 내의 술어와 이들의 논항들의 의미 관계를 결정하는 과정을 뜻한다. 의미역 결정을 하기 위해서는 대량의 말뭉치와 다양한 언어 자원이 필요한데, 많은 경우에 PropBank 말뭉치가 사용된다. 한국어 PropBank는 다른 언어에 비해 자료가 적어 그것만을 가지고 의미역 결정을 하기에 적절하지 않다. 또한 한국어 의미 분석을 위해서 지금까지는 세종 말뭉치나 의미역이 활용되어 오기도 하였다. 따라서 한국어 의미역 결정에서는 한국어 PropBank 뿐만 아닌 세종 의미역 표지 부착 말뭉치의 구축 역시 요구되는데 말뭉치 구축 작업이 수동 부착 작업이기 때문에 많은 시간과 비용이 소모된다. 본 논문에서는 이러한 문제점을 해결하기 위해 이미 구축되어 있는 한국어 PropBank 의미역을 세종 의미역으로 자동 변환하는 방법을 제시한다. 자동 변환을 위해서는 먼저 PropBank 의미역의 변환 후보 의미역을 구하여 이들 중에서 가장 적절한 의미역으로 변환한다. 자동 변환을 위해서는 크게 3 가지 특징을 활용하는데, 첫째는 변환 대상 논항의 의미 유사성이고, 둘째는 논항과 의미 관계를 가지고 있는 술어, 그리고 셋째는 논항과 결합되어 있는 조사이다. 이 세 가지 특징을 사용하여 정확한 의미역 변환을 위해 술어, 조사의 의미역 결합 확률 테이블을 구축한다.