• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.144-149
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• 2007

Grid technologies make it possible for IT resources to be shared across organizational and security domains. The traditional identity-based access control mechanisms are unscalable and difficult to manage. Thus, we propose the FAS (Federation Agent Server) model which is composed of three modules: Certificate Conversion Module (CCM), Role Decision Module (RDM), and Authorization Decision Module (ADM). The proposed FAS model is an extended Role-Based Access Control (RBAC) model which provides resource access capabilities based on roles assigned to the users. FAS can solve the problem of assigning multiple identities to a shared local name in grid-map file and mapping the remote entity's identity to a local name manually.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.95-100
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• 2003

In this paper, we propose solutions to resolve the problem of many spelling variants and the problem of lack of annotated corpus for training, which are two among the main difficulties in named entity recognition in biomedical domain. To resolve the problem of spotting valiants, we propose a use of edit-distance as a feature for SVM. And we propose a use of virtual examples to automatically expand the annotated corpus to resolve the lack-of-corpus problem. Using virtual examples, the annotated corpus can be extended in a fast, efficient and easy way. The experimental results show that the introduction of edit-distance produces some improvements in protein name recognition performance. And the model, which is trained with the corpus expanded by virtual examples, outperforms the model trained with the original corpus. According to the proposed methods, we finally achieve the performance 75.80 in F-measure(71.89% in precision,80.15% in recall) in the experiment of protein name recognition on GENIA corpus (ver.3.0).

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.353-360
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• 2005

Database Integration problems has been recognized as a critical issue for effective logistics service in logistics environment. However, researches related to effective methodology for this have been studied theoretically in the DB schema integration, are insufficient in the side of the system realization. The aim of this paper is to present a schema integration technique to integrate DB using XML(eXtensible Markup Language) in the part of practical DB integration, a quantitative methodology for the identification of conflict that is a representative problem on database integration. To achieve this aim, we extracted the entity name and attribute name from DB schema and suggested a quantitative methodology to easily fine name conflict that frequently give raise to a trouble when schema integration, based on the level of semantic similarity between attributes and entities.

• 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).

• Journal of KIISE
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• v.43 no.6
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• pp.678-685
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• 2016

Named Entity Recognition and Classification (NERC) is a task for recognition and classification of named entities such as a person's name, location, and organization. There have been various studies carried out on Korean NERC, but they have some problems, for example lacking some features as compared with English NERC. In this paper, we propose a method that uses word embedding as features for Korean NERC. We generate a word vector using a Continuous-Bag-of-Word (CBOW) model from POS-tagged corpus, and a word cluster symbol using a K-means algorithm from a word vector. We use the word vector and word cluster symbol as word embedding features in Conditional Random Fields (CRFs). From the result of the experiment, performance improved 1.17%, 0.61% and 1.19% respectively for TV domain, Sports domain and IT domain over the baseline system. Showing better performance than other NERC systems, we demonstrate the effectiveness and efficiency of the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.987-997
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• 2018

Blockchain has tamper-free, so many applications are developing to leverage tamper-free features of blockchain. MIT Media Labs proposed BlockCerts, educational certificate blockchain System, to solve problems of legacy certificate verifications. Existing educational certificate blockchain Systems are based on public blockchain such as bitcoin, Ethereum, so any entity can participate educational institute in principal. Moreover, the exisitng educational certricate blockchain system utilizes the integrity of blockchain, but the confidentiality of the educational certificate is not provided. This paper propose a digital certificate system based on private blockchain, name HyperCerts. Therefore, only trusted entity can participate in the private blockchain network, Hyperledger, as the issuer of digital certificate. Furthermore, the practical byzantine fault tolerance is used as consensus algorithm, HyperCerts reduce dramatically the latency of issuing digital certificate and required computing power. HyperCerts stores the hash value of digital certificate into the ledger, so breach of personal information by malicious entity in the private blockchain is protected.

• The KIPS Transactions:PartC
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• v.10C no.3
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• pp.273-280
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• 2003

When an existing user authorization systems in Grid access many user to local system and subject DN (Distinguished Name) in a user-proxy authenticate and ID in local system is one-to-one mapping, they have difficulties in ID management, memory resource management and resource management. At this, a variety of subject DN is shared of one local ID in an existing Grid. But this faces many difficulties in applying all requirements for many Grid users. Thus, we suppose user authorization system based on a certificate not them based on ID in this paper. That is, we add user's access level to extension field in a certificate, and make a supposed authorization system decide access limitation level on resources instead of an existing ID mapping methods.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.50-58
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• 2002

The information extraction is to delimit in advance, as part of the specification of the task, the semantic range of the output and to filter information from large volumes of texts. The most representative word of the document is composed of named entities and pronouns. Therefore, it is important to resolve coreference in order to extract the meaningful information in information extraction. Coreference resolution is to find name entities co-referencing real-world entities in the documents. Results of coreference resolution are used for name entity detection and template generation. This paper presents the heuristic-based approach for coreference resolution in Korean. We constructed the heuristics expanded gradually by using the corpus and derived the salience factors of antecedents as the importance measure in Korean. Our approach consists of antecedents selection and antecedents weighting. We used three kinds of salience factors that are used to weight each antecedent of the anaphor. The experiment result shows 80% precision.

• Korean Institute of Interior Design Journal
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• v.22 no.2
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• pp.100-111
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• 2013

The theories of optical/haptic perception provide us contrasting insights into the perception of space in movie and architecture. Through the lenses of these theories, this study aims to analyse the optical and haptical aspect of the medieval library of the film, The Name of the Rose. The dominance of vision over the other senses has been maintained by many philosophers, such as Plato, Aristotle, and Aquinas, and this trend leads to the development of the hierarchical and perspective space of Renaissance and Modern Architecture. Those conceptions of optical space help us not only identify space as clear and distinct three-dimensional entity but also separate the subject and the object. However, tactile/haptic perception is more useful to explain the experience of film and contemporary architecture than optical perception. This haptic space is developed by Alois Riegl, Walter Benjamin, and Gilles Deleuze. This study intends to search for the difference between two perceptions on the architectural space of the movie, examine the relation between architecture and human, space and user.