• 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 Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.55-62
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• 2022

A smart tourism chatbot is needed as a user interface to efficiently provide smart tourism services such as recommended travel products, tourist information, my travel itinerary, and tour guide service to tourists. We have been developed a smart tourism app and a smart tourism information system that provide smart tourism services to tourists. We also developed a smart tourism chatbot service consisting of khaiii morpheme analyzer, rule-based intention classification, and tourism information knowledge base using Neo4j graph database. In this paper, we develop the Korean and English smart tourism Name Entity (NE) datasets required for the development of the NER model using the pre-trained language models (PLMs) for the smart tourism chatbot system. We create the tourism information NER datasets by collecting source data through smart tourism app, visitJeju web of Jeju Tourism Organization (JTO), and web search, and preprocessing it using Korean and English tourism information Name Entity dictionaries. We perform training on the KoBERT-CRF NER model using the developed Korean and English tourism information NER datasets. The weight-averaged precision, recall, and f1 scores are 0.94, 0.92 and 0.94 on Korean and English tourism information NER datasets.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.236-244
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• 2022

Network Mobility Basic Support (NEMO BS) protocol has been accredited and approved by Internet Engineering Task Force (IETF) working group for mobility of sub-networks. Trains, aircrafts and buses are three examples of typical applications for this protocol. The NEMO BS protocol was designed to offer Internet access for a group of passengers in a roaming vehicle in an adequate fashion. Furthermore, in NEMO BS protocol, specific gateways referred to Mobile Routers (MRs) are responsible for carrying out the mobility management operations. Unfortunately, the main limitations of this basic solution are pinball suboptimal routing, excessive signaling cost, scalability, packet delivery overhead and handoff latency. In order to tackle shortcomings of triangular routing and Quality of Service (QoS) deterioration, the proposed scheme (Diff-FH NEMO) has previously evolved for end-users in moving network. In this sense, the article focuses on an exhaustive analytic evaluation at Home Agent (HA) entity of the proposed solutions. An investigation has been conducted on the signaling costs to assess the performance of the proposed scheme (Diff-FH NEMO) in comparison with the standard NEMO BS protocol and MIPv6 based Route Optimization (MIRON) scheme. The obtained results demonstrate that, the proposed scheme (Diff-FH NEMO) significantly improves the signaling cost at the HA entity in terms of the subnet residence time, number of mobile nodes, the number of DMRs, the number of LFNs and the number of CNs.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.633-642
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• 2006

In the Internet environment many interactions between many users and unknown users take place and it is usually rare to have the trust information about others. Due to the lack of trust information, entities have to take some risks in transactions with others. In this perspective, it is crucial for the entities to be equipped with functionality to accumulate and manage the trust information on other entities in order to reduce risks and uncertainty in their transactions. This paper is concerned with a quantitative computational trust model which takes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust for an entity. In the proposed trust model, the trust for an entity is defined as the expectation for the entity to yield satisfactory outcomes in the given situation. Once an interaction has been made with an entity, it is assumed that outcomes are observed with respect to evaluation criteria. When the trust information is needed, the satisfaction degree, which is the probability to generate satisfactory outcomes for each evaluation criterion, is computed based on the empirical outcome outcome distributions and the entity's preference degrees on the outcomes. Then, the satisfaction degrees for evaluation criteria are aggregated into a trust value. At that time, the reputation information is also incorporated into the trust value. This paper also shows that the model could help the entities effectively choose other entities for transactions with some experiments in e-commerce.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.29-35
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• 2020

In this paper, we propose a new structured entity recognition DeNERT model. Recently, the field of natural language processing has been actively researched using pre-trained language representation models with a large amount of corpus. In particular, the named entity recognition, which is one of the fields of natural language processing, uses a supervised learning method, which requires a large amount of training dataset and computation. Reinforcement learning is a method that learns through trial and error experience without initial data and is closer to the process of human learning than other machine learning methodologies and is not much applied to the field of natural language processing yet. It is often used in simulation environments such as Atari games and AlphaGo. BERT is a general-purpose language model developed by Google that is pre-trained on large corpus and computational quantities. Recently, it is a language model that shows high performance in the field of natural language processing research and shows high accuracy in many downstream tasks of natural language processing. In this paper, we propose a new named entity recognition DeNERT model using two deep learning models, DQN and BERT. The proposed model is trained by creating a learning environment of reinforcement learning model based on language expression which is the advantage of the general language model. The DeNERT model trained in this way is a faster inference time and higher performance model with a small amount of training dataset. Also, we validate the performance of our model's named entity recognition performance through experiments.

• ETRI Journal
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• v.44 no.5
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• pp.794-804
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• 2022

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.

• Archives of design research
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• v.13 no.4
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• pp.67-76
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• 2000

Websites support various user activities in a wide range of contents domain and so they require different approach to extract principal design problems. In the point of media perspective on websites, this paper figures out the relationship between designer, user and website and discusses design factors of usability. It aims for the basic framework for interface design. In the media perspective website is an information entity mediating user and designer. Information entity is composed of various design factors relating to user, designer, website and others. It intends that user and information entity are accommodative to each other and have common conceptual model. To do so it is necessary for achieving usability objectives such as effectiveness, efficiency and satisfaction based on the understanding user goal, cognitive and affective characteristics. In the point of usability we examine design factors and features that are appropriate for users cognitive and affective function according to information entity model that constitutes contents, organization and representation level.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.227-248
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• 2017

This paper introduces an integrated model for systematically constructing a linguistic resource database that can be used by machine learning-based biomedical information extraction systems. The proposed method suggests an orderly process of collecting and constructing dictionaries and training sets for both named-entity recognition and relation extraction. Multiple heterogeneous structures for the resources which are collected from diverse sources are analyzed to derive essential items and fields for constructing the integrated database. All the collected resources are converted and refined to build an integrated linguistic resource storage. In this paper, we constructed entity dictionaries of gene, protein, disease and drug, which are considered core linguistic elements or core named entities in the biomedical domains and conducted verification tests to measure their acceptability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.285-290
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• 2006

Various aspects should be taken into account while developing a distributed architecture based on a multi-tier model or an enterprise architecture. Among those, the separation of role between page designer and page developer, defining entity which is used for database connection and transaction processing are very much important. In this paper, we presented DONSL(Data Server of Non SQL query) architecture to solve these problems applying web tier object modelling. This architecture solves the above problems by simplifying tiers coupling and removing DAO(Data Access Object) and entity from programming logic. We concentrate upon these three parts. One is about how to develop the DAO not concerning the entity modification, another is automatic transaction processing technique including SQL generation and the other is how to use the AET/MET(Automated/Manual Execute d Transaction) effectively.