• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.229-243
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• 2006

This study aims to suggest an effective way to enhance question-answer(QA) document retrieval performance by reconstructing queries based on the structural features in the QA documents. QA documents are a structured document which consists of three components : question from a questioner, short description on the question, answers chosen by the questioner. The study proposes the methods to reconstruct a new query using by two major structural parts, question and answer, and examines which component of a QA document could contribute to improve query performance. The major finding in this study is that to use answer document set is the most effective for reconstructing a new query. That is, queries reconstructed based on terms appeared on the answer document set provide the most relevant search results with reducing redundancy of retrieved documents.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.23-32
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• 2010

In this paper, we first propose the security enhancing of authentication protocol for Hash based RFID tag, and then a digital Codec for RFID tag is designed based on the proposed authentication protocol. The protocol is based on a three-way challenge response authentication protocol between the tags and a back-end server. In order to realize a secure cryptographic authentication mechanism, we modify three types of the protocol packets which defined in the ISO/IEC 18000-3 standard. Thus active attacks such as the Man-in-the-middle and Replay attacks can be easily protected. In order to verify effectiveness of the proposed protocol, a digital Codec for RFID tag is designed using Verilog HDL, and also synthesized using Synopsys Design Compiler with Hynix $0.25\;{\mu}m$ standard-cell library. Through security analysis and comparison result, we will show that the proposed scheme has better performance in user data confidentiality, tag anonymity, Man-in-the-middle attack prevention, replay attack, forgery resistance and location tracking.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.269-278
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• 2003

The popularity of the web based education has come the need for variety learning methods and for business to exploit the web not only for interoperability but also standardization. This way of standardization has come to researched for environments, contents and practical uses in ISO. The IEEE has special]y established five technical classes for LTSA which provide advanced e-learning environments. Feedback functions would not be supported and specified in standardization for Query Answer on LTSA. In this paper, we describe the query and answer model which we have developed on layer three of LTSA. We develop the redefined model for transforming data flow oriented into object or component based model. We have developed the Query Answer Metadata (QAM) based on Learning Object Metadata (LOM). We design and showed thing a prototyping implementation the Query Answer Learning Tool (QALT). We have used the QALT to address the problem of efficiency of web based education. We also used it to develop the related tools with quality and productivity.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.