• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.61-65
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• 2006

This paper is concerned with a quantitative computational trust model which lakes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust value for entities. 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 outcome probability 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 presents in detail how the trust model works.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.13-20
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• 2022

This study is to develop a named entity recognition model specialized in criminal investigation domains using deep learning techniques. Through this study, we propose a system that can contribute to analysis of crime for prevention and investigation using data analysis techniques in the future by automatically extracting and categorizing crime-related information from text-based data such as criminal judgments and investigation documents. For this study, the criminal investigation domain text was collected and the required entity name was newly defined from the perspective of criminal analysis. In addition, the proposed model applying KoELECTRA, a pre-trained language model that has recently shown high performance in natural language processing, shows performance of micro average(referred to as micro avg) F1-score 98% and macro average(referred to as macro avg) F1-score 95% in 9 main categories of crime domain NER experiment data, and micro avg F1-score 98% and macro avg F1-score 62% in 56 sub categories. The proposed model is analyzed from the perspective of future improvement and utilization.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1291-1312
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• 2013

Cloud computing changes the service models of information systems and accelerates the pace of technological innovation of consumer electronics. However, it also brings new security issues. As one of the important foundations of various cloud security solutions, entity authentication is attracting increasing interest of many researchers. This article proposes a layered security architecture to provide a trust transmission mechanism among cloud systems maintained by different organizations. Based on the security architecture, four protocols are proposed to implement mutual authentication, data sharing and secure data transmission in federated cloud systems. The protocols not only can ensure the confidentiality of the data transferred, but also resist man-in-the-middle attacks and masquerading attacks. Additionally, the security properties of the four protocols have been proved by S-pi calculus formal verification. Finally, the performance of the protocols is investigated in a lab environment and the feasibility of the security architecture has been verified under a hybrid cloud system.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.32-39
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• 2020

In the large-scale infrastructure of cloud environment, illegal access rights are frequently caused by sharing applications and devices, so in order to actively respond to such attacks, a strengthened access control system is required to prepare for each situation. We proposed an entity attribute-based access control(EABAC) model based on security level and relation concept. This model has enhanced access control characteristics that give integrity and confidentiality to subjects and objects, and can provide different services to the same role. It has flexibility in authority management by assigning roles and rights to contexts, which are relations and context related to services. In addition, we have shown application cases of this model in multi service environment such as university.

• International Journal of Contents
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• v.8 no.4
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• pp.21-29
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• 2012

It is not easy for users to effectively have information that is semantically related but scattered on the Web. To obtain qualitatively improved information in web pages, it is necessary to integrate information that is heterogeneous but semantically related. In this study, we propose a method that provides XML-based metadata to users through integration of multiple heterogeneous Web pages. The metadata generated from the proposed system is obtained by integrating different heterogeneous information into a single page, using entity identification based on ontology. A wheelchair information integration system for disabled people is implemented to verify the efficiency of the proposed method. The implemented system provides an integrated web page from multiple web pages as a type of XML document.

• Genomics & Informatics
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• v.17 no.2
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• pp.15.1-15.7
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• 2019

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).

• 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.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.67-77
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• 2018

This paper suggests that LOD establishes its own link policy and publishes it to LOD cloud to provide identity among entities in different LODs. For specifying the link policy, we proposed vocabulary set founded on RDF model as well. We implemented Policy based In-depth Searching and Cleansing(PISC for short) system that proceeds in-depth searching across LODs by referencing the link policies. PISC has been published on Github. LODs have participated voluntarily to LOD cloud so that degree of the entity identity needs to be evaluated. PISC, therefore, evaluates the identities and cleanses the searched entities to confine them to that exceed user's criterion of entity identity level. As for searching results, PISC provides entity's detailed contents which have been collected from diverse LODs and ontology customized to the content. Simulation of PISC has been performed on DBpedia's 5 LODs. We found that similarity of 0.9 of source and target RDF triples' objects provided appropriate expansion ratio and inclusion ratio of searching result. For sufficient identity of searched entities, 3 or more target LODs are required to be specified in link policy.