• Journal of KIISE
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• v.43 no.12
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• pp.1376-1384
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• 2016

Computers require analytic and processing capability for all possibilities of human expression in order to process sentences like human beings. Linguistic information processing thus forms the initial basis. When analyzing a sentence syntactically, it is necessary to divide the sentence into components, find obligatory arguments focusing on predicates, identify the sentence core, and understand semantic relations between the arguments and predicates. In this study, the method applied a case frame dictionary based on The Korean Standard Dictionary of The National Institute of the Korean Language; in addition, we used a CRF Model that constructed subcategorization of predicates as featured in Korean Lexical Semantic Network (UWordMap) for semantic role labeling. Automatically tagged semantic roles based on the CRF model, which established the information of words, predicates, the case-frame dictionary and hypernyms of words as features, were used. This method demonstrated higher performance in comparison with the existing method, with accuracy rate of 83.13% as compared to 81.2%, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.277-288
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• 2021

Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.521-525
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• 2021

An essential area is the creation of a single digital market between the EU and Ukraine through information technology. Purpose: to investigate and analyze civil law relations in the field of adaptation of Ukrainian civil law to civil law regulations of the EU. The object of research: Ukrainian civil law and civil law of the EU. The subject of the study is civil law in the context of adaptation of civil law to the legislation of the EU. The following methods of scientific cognition were used during the research: semantic, historical, comparison, analysis and synthesis, generalization. The results of the study show that the harmonization of the legal system of Ukraine with EU law is caused by several goals: successful integration of Ukraine into the EU, legal reforms based on the positive example of EU countries, promoting access of Ukrainian enterprises to the EU market; attracting foreign investment, increasing the welfare of Ukrainian citizens. The adaptation includes three stages, the final of which is the preparation of an expanded program of harmonization of Ukrainian legislation with EU legislation. In the process of adaptation, it is important to take into account the legal history, tradition, features and mentality of Ukraine and before borrowing legal structures to analyze the feasibility of their application in the Ukrainian legal field.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.51-64
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• 1988

A new approach to the logical design of 4NF database shceme, which can be easily automated, is proposed. The main features of the approach are : introduction of a single attribute right hand side, extension of the concept of independent relations, semantic analysis, and adaoption of dependency matrix. The underlying viewpoints of functional relationships of the approach are different from Fagin's in that we distinguish functional and multivalued dependency in terms of cardinality. An algorithm for automatic generation of fourth normal form is presented and implemented.

• The Journal of Korean Medicine
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• v.32 no.1
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• pp.121-129
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• 2011

Objective: The propose of this study is to build a conceptual framework for representing clinical findings in Traditional East Asian Medicine(TEAM). As the existing standard models have been developed without considering features of Traditional Medicine, in this study we introduced unique characteristics for the TEAM. Method: This study was composed of three steps. First, we analyzed whether the existing clinical information models are suitable for representing clinical findings. Second, we analyzed ISO/TS 22789 model which is a ISO medical informatics standard, to find out the problem by applying clinical findings of TEAM into the model. Finally, we defined semantic links and a concept hierarchy in our model based on the analyzed results. The model includes the concepts for clinical findings and terms, and the semantic links can be regarded as relations between concepts, so that the representating clinical findings are completed by connecting concepts with other concepts. Results: Our framework was developed by removing unnecessary semantic links, and adding some necessary ones based on ISO/TS 22789 model. The ISO/TS 22789 model has a simple concept hierarchy, but in this study we subdivided the hierarchy and also considered interoperability with other terminological systems and standard models. Conclusions: This research needs more discussions, but is meaningful as proposing a way how to develop Traditional Medicine terminological systems. This study shows the limitations of existing models in describing clinical findings for TEAM, and what should be considered to represent Traditional Medicine knowledge, and propose a solution to improve the problem.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.69-78
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• 2016

Ischemic stroke is a disease that the brain tissues cannot function by reducing blood flow due to thrombosis or embolisms. Due to the nature of the disease, it is most important to identify the status of cerebral vessel and the medical images are necessarily used for its diagnosis. Among many indicators, brain MRI is most widely utilized because experts can effectively obtain the semantic information such as cerebral anatomy aiding the diagnosis with it. However, in case of emergency diseases like ischemic stroke, even though a intelligent system is required for supporting the prompt diagnosis and treatment, the current systems have some difficulties to provide the information of medical images intuitively. In other words, as the current systems have managed the medical images based on the basic meta-data such as image name, ID and so on, they cannot consider semantic information inherent in medical images. Therefore, in this paper, to provide core information like cerebral anatomy contained in brain MRI, we suggest a template-driven medical images mapping method. The key idea of the method is defining the mapping characteristics between anatomic feature and representative images by using template images that can be representative of the whole brain MRI image set and revealing the semantic relations that only medical experts can check between images. With our method, it will be possible to manage the medical images based on semantic.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.36-45
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• 2009

Recently, there are interested in the automatic traffic flowing and accident detection using various low level information from video in the road. In this paper, the automatic traffic flowing and algorithm, and application of traffic accident detection using traffic management systems are studied. To achieve these purposes, the spatio-temporal relation models using topological and directional relations have been made, then a matching of the proposed models with the directional motion verbs proposed by Levin's verbs of inherently directed motion is applied. Finally, the synonym and antonym are inserted by using WordNet. For the similarity measuring between proposed modeling and trajectory of moving object in the video, the objects are extracted, and then compared with the trajectories of moving objects by the proposed modeling. Because of the different features with each proposed modeling, the rules that have been generated will be applied to the similarity measurement by TSR (Tangent Space Representation). Through this research, we can extend our results to the automatic accident detection of vehicle using CCTV.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.187-193
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• 2008

Nowadays, the study on e semantic web has been actively progressing for processing the web data semantically. For actualizing the semantic web environment, the core task is to build the ontology that defines the concepts and relations between concepts about the all things. Many ontology languages such as OWL, RDF(S), DAML+OIL were developed for building the ontology. And the many ontology tools were also implemented based on them. Although, many language and tools were researched, the practical use of the ontology tools is limited to the experts and researchers about the ontology because of the difficulty of the vocabulary, weak understanding about the ontology theory and the difficulty of the use of the ontology tools. And there are no studies on the reuse of constructed huge ontology. Therefore, in our study we design and implement the OWL ontology management tool that both the ontology experts and general users who want to build the ontologies are able to construct the ontology easily In this paper, we introduce the main modules used in our tool and features of our tool.

• Journal of the Korean Society for information Management
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• v.21 no.2
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• pp.249-267
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• 2004

Although MARC can define the detail cataloguing data, it has complex structures and frameworks to represent bibliographic information. On account of these idiosyncratic features of MARC, XML DTD or RDF/S that supports simple hierarchy of conceptual vocabularies cannot capture MARC formalism effectively. This study implements bibliographic ontology by means of abstracting conceptual relationships between bibliographic vocabularies of MARC. The bibliographic ontology is formalized with OWL that can represent the logical relations between conceptual elements and specify cardinality and property value restrictions. The bibliographic ontology in this study will provide metadata for cataloguing data and resolve compatibility problems between cataloguing systems. And it can also contribute the development of next generation bibliographic information system using semantic Web services.