• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.165-187
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• 2018

Despite repeated efforts to develop a methodological foundation for assembling collaborative authority data in South Korea, issues such as the establishment of a standard authority model and standard authority construction as well as the reconfiguration of existing entities in authority building have prevented such research from generating a cooperative push for nation-wide authority data and progressing toward concrete implementation. The formulation of a collaborative and well-utilized collection of national authority data accordingly calls for 1) a practical approach to supporting both established authority data contributors and newly organized avenues of mutual participation in authority building, 2) committed involvement on the part of national institutions capable of providing the project with sustained assistance, and 3) a standard identification system which allows multiple organizations to merge their data. This study addresses the challenges of the current environment by taking stock of the key components necessary for the creation of collaborative authority data and using a Semantic Web-based interoperable VIVO ontology model to propose a viable national authority data framework.

• ETRI Journal
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• v.35 no.1
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• pp.41-50
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• 2013

Network Mobility (NEMO) handles mobility of multiple nodes in an aggregate manner as a mobile network. The standard NEMO suffers from a number of limitations, such as inefficient routing and increased handoff latency. Most previous studies attempting to solve such problems have imposed an extra signaling load and/or modified the functionalities of the main entities. In this paper, we propose a more secure and lightweight route optimization (RO) mechanism based on exploiting the firewall in performing the RO services on behalf of the correspondent nodes (CNs). The proposed mechanism provides secure communications by making an authorized decision about the mobile router (MR) home of address, MR care of address, and the complete mobile network prefixes underneath the MR. In addition, it reduces the total signaling required for NEMO handoffs, especially when the number of mobile network nodes and/or CNs is increased. Moreover, our proposed mechanism can be easily deployed without modifying the mobility protocol stack of CNs. A thorough analytical model and network simulator (Ns-2) are used for evaluating the performance of the proposed mechanism compared with NEMO basic support protocol and state-of-the-art RO schemes. Numerical and simulation results demonstrate that our proposed mechanism outperforms other RO schemes in terms of handoff latency and total signaling load on wired and wireless links.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.285-293
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• 2008

In Semantic Web, metadata and ontology for representing semantics and conceptual relationships of information resources are essential factors. RDF and RDF Schema are W3C standard models for describing metadata and ontology. Therefore, many studies to store and retrieve RDF and RDF Schema documents are required. In this paper, we focus on some results of analyzing available query patterns considering both RDF and RDF Schema and classify queries on RDF and RDF Schema into the three patterns. RDF and RDF Schema can be represented as graph models. So, we proposed some strategies to store and retrieve using the graph models of RDF and RDF Schema. We can retrieve entities that can be arrived from a certain class or property in RDF and RDF Schema without a loss of performance on account of multiple joins with tables.

• 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 KIPS Transactions:PartD
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• v.11D no.5
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• pp.1095-1104
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• 2004

An electronic services which are based on Internet/Intranet business transactions are available to e-market places and get the broader business concepts. Component-based e-commerce technology is a recent trend towards resolving the e-commerce chanange at both system and ap-plication levels. The basic capabilities of component based systems should include the plug and play features at various granularities, interoper-ability across networks and mobility in various networking environments. E-business application developers are attempting to more towards web-service as a mechanism for developing component-based web-applications. Traditional process and development models are inadequately architectured to meet the rapidly evolving needs for the future of scalable web services. In this thesis, we focus specifically on the issues of e-business system architecture based on web service for establishing e-business system. We specifies and identifies design pattern for applying e-business domain in the context of multiple entities. We investigate prototype and frameworks to develope components for e-business application based suggested process. We present a worked example to demonstrate the behavior of Customer Authentication System(CAS) development with component and recommend process. Final]Y, we indicate and view on future directions in component-based development in the context of electronic business.

• Smart Media Journal
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• v.12 no.4
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• pp.27-40
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• 2023

Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.

• The Korean Journal of Archival Studies
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• no.33
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• pp.155-206
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• 2012

Interests in archives management are getting expanded from the public sector into the cultural and artistic field for the ten years after legislation of "Act on the Management of Public Archives" in 1999. However, due to lack of recognition on the importance of archives in the cultural and artistic field, it is rather frequent that information is kept scattered or archives are lost. As an example, absence of precise contract documents or notes of bestowal keeps people from locating great amount of cultural properties, and because of it these creative properties are in the risk of thefts, the closed-door auctioning, or trades in unofficial channels. As how a nation manages cultural and artistic creation inside the nation reflects its cultural level, it can be said that one of the indexes to notice the extent of a nation's cultural level is to take a look at how they are circulated. This study started from this point. Growing economy and rising interests in culture and art made the society more cognizant of the importance and value that visual artworks have, but the archives and information which are showing the context of these artworks and are produced in the course of social interaction are relatively disregarded because too much emphasis lies on the work itself. It is harder to find archives or documentations in Korea than in other advanced countries about the artists themselves or philosophical discourse on the background of the artworks. There is not so much interest to preserve the archives and information produced after the exhibition also, and they are used for no more than promotion or reference. Hereupon, the researcher recognized the importance of visual arts archives and believed that systemic management on them are high in need. And metadata is an essential way for the systemic management, as recently management on artworks or their archives are conducted using the system of the agencies even though they are not produced electronically. The objective of this study is to manage visual arts archives systematically by designing a data model reflecting traits of visual arts archives. Metadata are needed in the every course of archives from acquisition to management, preservation and application. Visual arts archives find its rich value only when a systemic relationship is established among information on artist, artwork and events including exhibition. By establishing a Multiple Entity Data Model, in which artworks, artists and events (exhibitions) make relationship all together, metadata for management on visual arts archive gets more efficiency and at the same time explanatory trait of the archive gets higher. For this reason we, in the study, tried to design a data model by setting each as an independent entities and designating relations between them, in order to find a way to manage visual arts archives more systematically.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.93-114
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• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.3
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• pp.61-78
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• 2021

Records on Japanese "Comfort Women" have been individually managed by private sectors or institutions, and some are provided as digital archives on the Internet. However, records of digital archives differ in the composition and representation of metadata by individual institutions. Meanwhile, there is a lack of a consistent structure to describe the relationships between and among these records, leading to their fragmentation and disconnectedness. This paper proposes a knowledge model for interlinking the digital archival resources and builds a knowledge graph by integrating the records from distributed digital archives. It derives common elements by analyzing metadata from the diverse digital archives and expresses them in standard vocabularies to semantically describe multiple entities and relationships of the digital archival resources. In particular, the study includes the refinement of collected data to search and thread dispersed records and the enrichment of external data to provide significant contextual information of records. An evaluation of the knowledge graph is performed via a query measuring the (dis)connectivity between the distributed records. As a result, the knowledge graph is capable of interlinking and retrieving fragmented records, providing substantial contextual information on the records with external data enrichment, and searching accurately to match the user's intentions through semantic-based queries.