• Journal of Information Processing Systems
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• v.15 no.4
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• pp.931-956
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• 2019

Documents contain information that can be used for various applications, such as question answering (QA) system, information retrieval (IR) system, and recommendation system. To use the information, it is necessary to develop a method of extracting such information from the documents written in a form of natural language. There are several kinds of the information (e.g., temporal information, spatial information, semantic role information), where different kinds of information will be extracted with different methods. In this paper, the existing studies about the methods of extracting the temporal information are reported and several related issues are discussed. The issues are about the task boundary of the temporal information extraction, the history of the annotation languages and shared tasks, the research issues, the applications using the temporal information, and evaluation metrics. Although the history of the tasks of temporal information extraction is not long, there have been many studies that tried various methods. This paper gives which approach is known to be the better way of extracting a particular part of the temporal information, and also provides a future research direction.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.1-12
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• 2024

Nowadays, Islamic content is widely used in research, including Hadith and the Al-Quran. Both are mostly used in the field of natural language processing, especially in text classification research. One of the difficulties in learning the Al-Quran is ambiguity, while the Al-Quran is used as the main source of Islamic law and the life guidance of a Muslim in the world. This research was proposed to relieve people in learning the Al-Quran. We proposed a word embedding feature-based on Tensor Space Model as feature extraction, which is used to reduce the ambiguity. Based on the experiment results and the analysis, we prove that the proposed method yields the best performance with the Hamming loss 0.10317.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.727-730
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• 2024

최근 딥러닝 기술이 급속히 발전하며 현대 사회의 다양한 응용분야에서 빠르게 적용되고 있다. 특히 영상 기반의 딥러닝 기술은 자연어 처리와 함께 인공지능 기술의 핵심 연구 분야로 많은 연구가 진행되고 있다. 논문에서는 최근 많은 연구가 진행되고 있는 영상의 의미적 분할 (Semantic Segmentation) 성능을 향상하기 위한 연구를 진행한다. 특히 모델에서 고정밀의 의미적 분할을 수행할 수 있도록 추가적인 정보로써 의사 깊이맵 (Pseudo Depth-Map)을 활용하는 방법을 제안하였다. 더불어, 의사 깊이맵을 모델 상에서 효과적으로 학습시키기 위하여 다중 디코더 모델과 학습 효율을 높이는 학습 스케줄링 전략을 제안한다. 의사 깊이맵과 다중 디코더 모델 기반의 제안 모델은 기존 의미적 분할 모델과 비교하여 iIoU 기준 2%의 성능 향상을 보였다.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.192-201
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• 2005

For the past ten years, the goal of ubiquitous computing research has been the establishment of a new technology system with the aim 'Anytime, Anywhere, and Any form'. The needs for agent technology innovations such as ontology-based structure, ontology-based agent communication language, and multi-agents frameworks have been identified. This paper proposes a noble multi-agents architecture for ubiquitous systems. We suggest four major steps in the interaction between human and agents which enable ubiquitous agents to process by themselves to provide adaptive service to meet human's needs. First, we propose a semantic web technology to represent the association between information resources more explicitly Second, we construct a semantic ontology so that agents can recognize web contents.'Third, we propose a method to communicate between agents using OWL ontologies. Finally, we suggest a multi-agents structure based on the JADE of FIPA to analyze messages and get information. The semantic multi-agents framework proposed in this paper infers semantic situations using semantic web technology based on ontologies. A service provided is inferred differently according to user state because the multi-agents communicate by using OWL ontology language. Therefore, our system better infers context information than other without ontologies.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.592-594
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• 2005

XML은 현재 어플리케이션에서 데이터를 저장하는 W3C 표준으로 많은 응용 분야에서 사용되고 있다. 어떤 응용 프로그램에서 사용하던 기존의 XML문서를 다른 응용 프로그램에서 재사용하기 위해서는 변환 XML 문서가 가지고 있는 정보와 구조의 손실 없이 피 변환 XML의 구조에 합당하게 변환해야 한다. XML 문서 정보의 의미는 엘리먼트를 통해 표현되는데 자동으로 분석하여 변환에 적용하기 어렵다. 그러나 XML 문서는 DTD나 XML 스키마와 같은 구조적 정보를 가지고 있고 XML의 구조 정보는 엘리먼트에 속성을 표현한다. 이에 착안하여 DTD의 의미정보를 분석하여 XML 문서의 변환에 적용하는 방법이 제안되었다. 하지만 DTD는 지원하는 데이터 형식이 한정되어 있고 엘리먼트의 반복 속성도 제안되어 있다. 본 논문은 XML의 엘리먼트 정보를 분석하기 위해서 XML 스키마를 사용한다. XML 스키마는 기존에 DTD보다 다수의 데이터 타일과 엘리먼트의 반복적 속성을 다양하게 제공하고 있다. 그러므로 기존 방법보다 더 많은 정보를 변환에 적용할 수 있는 장점을 가지고 있다. 제안하는 시스템은 한번 작성한 XML 문서를 다른 XML 어플리케이션에서 재사용함으로써 XML 문서 제작성에 따른 비용을 절감할 것으로 기대된다.

• The KIPS Transactions:PartA
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• v.15A no.3
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• pp.151-154
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• 2008

A middleware in grid computing environment is required to support seamless on-demand services over diverse resource situations in order to meet various user requirements [1]. Since grid computing applications need situation-aware middleware services in this environment. In this paper, we propose a semantic middleware architecture to support dynamic software component reconfiguration based fault and service ontology to provide fault-tolerance in a grid computing environment. Our middleware includes autonomic management to detect faults, analyze causes of them, and plan semantically meaningful strategies to recover from the failure using pre-defined fault and service ontology trees. We implemented a referenced prototype, Web-service based Application Execution Environment(Wapee), as a proof-of-concept, and showed the efficiency in runtime recovery.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.221-224
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• 2014

의미 표지 부착 작업은 구문 표지 부착된 문장의 술어-논항 구조를 파악하여 논항에 적절한 의미역을 부착하는 과정이다. 이 작업을 통하여 생성되는 의미 표지 부착 말뭉치는 의미역 결정에 있어서 절대적으로 필요한 자원이 된다. 의미 표지 부착 말뭉치로는 세계적으로 PropBank가 널리 활용되고 있는데 이를 한국어에 적용시키기 위해서는 PropBank 의미역과 Sejong 의미역 간의 자동 변환이 필요하다. 이전에 제안되었던 이종 의미역 간의 자동변환 방법에서는 명사 계층의 구조 정보를 반영하지 않았다는 문제점이 있었다. 본 논문에서는 이러한 문제점을 보강하기 위하여 명사 계층구조를 반영하여 한국어 PropBank 의미역을 Sejong 의미역으로 자동 변환하는 방법을 제안한다. 제안하는 방법은 PropBank와 Sejong의 맵핑관계 중에서 1:N으로 맵핑되는 PropBank 의미역을 기준으로 명사 계층구조에서 변환 대상 의미역을 가지고 있는 단어와 변환 후보 의미역을 가진 단어들의 개념번호를 뽑아 두 단어 간의 거리를 측정한다. 그리고 레벨 당 가중치를 주어 유사도 계산을 하여 유사도가 적은 값으로 의미역을 자동 변환한다. 본 논문에서 제안하는 방법은 0.8의 성능을 보인다.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.567-582
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• 2011

The growing popularity of the Internet has caused the size and complexity of communications and computing systems to greatly increase in recent years. To alleviate this increased management complexity, novel autonomic management architectures have emerged, in which many automated components manage the network's resources in a distributed fashion. However, in order to achieve effective collaboration between these management components, they need to be able to efficiently exchange information in a timely fashion. In this article, we propose a context dissemination framework that addresses this problem. To achieve scalability, the management components are structured in a hierarchy. The framework facilitates the aggregation and translation of information as it is propagated through the hierarchy. Additionally, by way of semantics, context is filtered based on meaning and is disseminated intelligently according to dynamically changing context requirements. This significantly reduces the exchange of superfluous context and thus further increases scalability. The large size of modern federated cloud computing infrastructures, makes the presented context dissemination framework ideally suited to improve their management efficiency and scalability. The specific context requirements for the management of a cloud data center are identified, and our context dissemination approach is applied to it. Additionally, an extensive evaluation of the framework in a large-scale cloud data center scenario was performed in order to characterize the benefits of our approach, in terms of scalability and reasoning time.

• Journal of the Korean Society for information Management
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• v.28 no.4
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• pp.263-278
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• 2011

This study analyzed the level of 326 academic librarians awareness, usability and counter strategies on the Library 3.0. The results revealed the awareness level in the following order: that (1) Mobile Library (2) Semantic Search (3) AI(Artificial Intelligent) (4) Cloud Computing (5) Ontology (6) Linked Data. The order of the future usability for the Library 3.0 was ranked (1) Mobile Library (2) Linked Data (3) Semantic Search (4) Cloud Computing (5) AI and (6) Ontology. To conclude, the level of awareness and the usability of Library 3.0 were shown to be statistically significant. There are, however, some discrepancies in awareness differ across librarians and regions. Moreover, the level of awareness for the Library 3.0 did influence the library's organizational performance but the individual librarian's competences only.

• Journal of KIISE
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• v.43 no.1
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• pp.27-33
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• 2016

Virtualization has become one of the most helpful techniques in computing systems, and today it is prevalent in several computing environments including desktops, data-centers, and enterprises. However, since I/O layers are implemented to be oblivious to the I/O behaviors on virtual machines (VM), there still exists an I/O scalability issue in virtualized systems. In particular, when a multi-queue solid state drive (SSD) is used as a secondary storage, each system reveals a semantic gap that degrades the overall performance of the VM. This is due to two key problems, accelerated lock contentions and the I/O parallelism issue. In this paper, we propose a novel approach, including the design of virtual CPU (vCPU)-dedicated queues and I/O threads, which efficiently distributes the lock contentions and addresses the parallelism issue of Virtio-blk-data-plane in virtualized environments. Our approach is based on the above principle, which allocates a dedicated queue and an I/O thread for each vCPU to reduce the semantic gap. Our experimental results with various I/O traces clearly show that our design improves the I/O operations per second (IOPS) in virtualized environments by up to 155% over existing QEMU-based systems.