• Genomics & Informatics
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• v.12 no.1
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• pp.35-41
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• 2014

Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes were identified as follows: 22 genes in the USA (CEU) individuals, 25 genes in the Japanese (JPT) individuals, and 332 genes in the African (YRI) individuals. To analyze the biologically functional implications for ethnicity-specific SNPs, we focused on constructing a semantic network model. Entities for the network represented by "Gene," "Pathway," "Disease," "Chemical," "Drug," "ClinicalTrials," "SNP," and relationships between entity-entity were obtained through curation. Our semantic modeling for ethnicity-specific SNPs showed interesting results in the three categories, including three diseases ("AIDS-associated nephropathy," "Hypertension," and "Pelvic infection"), one drug ("Methylphenidate"), and five pathways ("Hemostasis," "Systemic lupus erythematosus," "Prostate cancer," "Hepatitis C virus," and "Rheumatoid arthritis"). We found ethnicity-specific genes using the semantic modeling, and the majority of our findings was consistent with the previous studies - that an understanding of genetic variability explained ethnicity-specific disparities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• Language and Information
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• v.2 no.1
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• pp.157-176
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• 1998

This paper claims that beneficiary adjuncts are best analyzed as involving external predication in a version of grammatical framework called Head-driven Phrase Structure Grammar. This paper also claims that verbal catefories need to include the attribute INDEX among their semantic components in order to account for the external predication proposed in this paper. This paper distinguishes between recipient and beneficiary reles and assumes that the former is a semantic argument of a verb-type relation and that the latter is an adjunct which makes a semantic contribution as a modifier. This approach achives a unified analysis of modification phenomena of nominal and verbal categories and it can also accomodate Parson's(1990) idea that a verbal category denotes a set of events, not just an event.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.283-292
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• 2019

The loss of semantic knowledge and impairments in semantic associations by semantic category is gaining increasing attention, as indicators of early-stage cognitive decline. As such, we assigned semantic association task (SAT) to normal elderly (NE) and those with subjective memory impairment (SMI) or mild cognitive impairment (MCI) to examine their performance by semantic subcategories and the differences in error patterns. We found a significant difference in the number of correct response and reaction time under the SAT categories among the three groups, with the highest performance observed in 'function' and the lowest performance in 'superordinate' and 'part/whole'. Moreover, the error frequency was the lowest in NE, followed by those with SMI and MCI, with the latter two groups showing a significant increase in no-response. Our findings demonstrate the varying extent and process of impairments in the semantic network by category over different stages of cognitive decline. Thus, we proposed SAT performance as an indicator to detect and follow-up on cognitive decline in elderly with cognitive disorder.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.91-96
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• 2019

In this study, a system to increase the expressiveness of existing standard terminology using three-dimensional (3D) data is designed. We analyze the existing medical terminology system by searching the reference literature and perform an expert group focus survey. A human body image is generated using a 3D modeling tool. Then, the anatomical position of the human body is mapped to the 3D coordinates' identification (ID) and metadata. We define the term to represent the 3D human body position in a total of 12 categories, including semantic terminology entity and semantic disorder. The Blender and 3ds Max programs are used to create the 3D model from medical imaging data. The generated 3D human body model is expressed by the ID of the coordinate type (x, y, and z axes) based on the anatomical position and mapped to the semantic entity including the meaning. We propose a system of standard terminology enabling integration and utilization of the 3D human body model, coordinates (ID), and metadata. In the future, through cooperation with the Electronic Health Record system, we will contribute to clinical research to generate higher-quality big data.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.245-252
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• 2022

Risk identification for bridge projects is a knowledge-based and labor-intensive task involving several procedures and stakeholders. Presently, risk information of bridge projects is unstructured and stored in different sources and formats, hindering knowledge sharing, reuse, and automation of the risk identification process. Consequently, there is a need to develop structured and formalized risk information for bridge projects to aid effective risk identification and automation of the risk management processes to ensure project success. This study proposes a semantic risk breakdown structure (SRBS) to support risk identification for bridge projects. SRBS is a searchable hierarchical risk breakdown structure (RBS) developed with python programming language based on a semantic modeling approach. The proposed SRBS for risk identification of bridge projects consists of a 4-level tree structure with 11 categories of risks and 116 potential risks associated with bridge projects. The contributions of this paper are threefold. Firstly, this study fills the gap in knowledge by presenting a formalized risk breakdown structure that could enhance the risk identification of bridge projects. Secondly, the proposed SRBS can assist in the creation of a risk database to support the automation of the risk identification process for bridge projects to reduce manual efforts. Lastly, the proposed SRBS can be used as a risk ontology that could aid the development of an artificial intelligence-based integrated risk management system for construction projects.

• Language and Information
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• v.7 no.2
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• pp.117-137
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• 2003

This paper attempts to provide an alternative analysis involving categorical issues related to English gerunds. Especially, this paper rejects Maulof's approach that creates a new syntactic category gerund by mixing nominal and verbal categories. This paper identifies two syntactic structures in English gerunds: nominal gerunds and verbal gerunds. This distinction is based on syntactic and semantic characteristics of each type and is intended to account for the external distribution and endocentricity of the construction. Treating verbal gerunds syntactically as verbal categories, this paper proposes that English verbal gerunds act like other verbal categories such as infinitives whereas nominal gerunds behaves much like derived nominals. This paper proposes a few lexical rules that can take care of the two types of gerunds. The proposal can be extended to prepositional complements as well as sentential subject positions. This proposal not only resolves the issues involving distributional properties of the gerund construction but also captures syntactic parallelism observable between gerunds and other verbal constructions in English.

• Journal of the Korean Society for information Management
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• v.23 no.1 s.59
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• pp.83-98
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• 2006

This study proposes a template-based method of automatic summarization of multiple news articles using the semantic categories of sentences. First, the semantic categories for core information to be included in a summary are identified from training set of documents and their summaries. Then, cue words for each slot of the template are selected for later classification of news sentences into relevant slots. When a news article is input, its event/accident category is identified, and key sentences are extracted from the news article and filled in the relevant slots. The template filled with simple sentences rather than original long sentences is used to generate a summary for an event/accident. In the user evaluation of the generated summaries, the results showed the 54.l% recall ratio and the 58.l% precision ratio in essential information extraction and 11.6% redundancy ratio.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.50-55
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• 2016

To attain an understanding of customers' opinions or demands regarding a companies' products or service, it is important to consider VOC (Voice of Customer) data; however, it is difficult to understand contexts from VOC because segmented and duplicate sentences and a variety of dialog contexts. In this article, POS (part of speech) and morphemes were selected as language resources due to their semantic importance regarding documents, and based on these, we defined an LSP (Lexico-Semantic-Pattern) to understand the structure and semantics of the sentences and extracted summary by key sentences; furthermore the LSP was introduced to connect the segmented sentences and remove any contextual repetition. We also defined the LSP by categories and classified the documents based on those categories that comprise the main sentences matched by LSP. In the experiment, we classified the VOC-data documents for the creation of a summarization before comparing the result with the previous methodologies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.167-173
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• 2011

Most of the terminologies used in medical domain is not intended to be applied directly in clinical setting but is developed to integrate the terms by defining the reference terminology or concept relations between the terms. Therefore, it is needed to develop the subsets of the terminology which classify categories properly for the purpose of use and extract and organize terms with high utility based on the classified categories in order to utilize the clinical terms conveniently as well as efficiently. Moreover, it is also necessary to develop and upgrade the terminology constantly to meet user's new demand by changing or correcting the system. This study has developed a mapping tool that allows accurate expression and interpretation of clinical terms used for medical records in electronic medical records system and can furthermore secure semantic interoperability among the terms used in the medical information model and generate common terms as well. The system is designed to execute both 1:1 and N:M mapping between the concepts of terms at a time and search for and compare various terms at a time, too. Also, in order to enhance work consistency and work reliability between the task performers, it allows work in parallel and the observation of work processes. Since it is developed with Java, it adds new terms in the form of plug-in to be used. It also reinforce database access security with Remote Method Invocation (RMI). This research still has tasks to be done such as complementing and refining and also establishing management procedures for registered data. However, it will be effectively used to reduce the time and expenses to generate terms in each of the medical institutions and improve the quality of medicine by providing consistent concepts and representative terms for the terminologies used for medical records and inducing proper selection of the terms according to their meaning.