• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1499-1507
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• 2009

With the abundance of digital contents, the necessity of precise indexing technology is consistently required. To meet these requirements, the intelligent software entity needs to be the subject of information retrieval and the interoperability among intelligent entities including human must be supported. In this paper, we analyze the unifying framework for multi-modality indexing that Snoek and Worring proposed. Our work investigates the method of improving the authenticity of indexing information in contents-based automated indexing techniques. It supports the creation and control of abstracted high-level indexing information through ontological concepts of Semantic Web skills. Moreover, it attempts to present the fundamental model that allows interoperability between human and machine and between machine and machine. The memory-residence model of processing ontology is inappropriate in order to take-in an enormous amount of indexing information. The use of ontology repository and inference engine is required for consistent retrieval and reasoning of logically expressed knowledge. Our work presents an experiment for storing and retrieving the designed knowledge by using the Minerva ontology repository, which demonstrates satisfied techniques and efficient requirements. At last, the efficient indexing possibility with related research is also considered.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.185-193
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• 2008

In spite of rapid development of IT-related meteorology and services, human users still ought to check the weather information manually as they did before because traditional weather information retrieval is based on pull-type and human interpretation. Furthermore, the automatic machine-driven weather information processing has been neglected for a long time although the intelligent weather information processing is expected to be very useful for personal daily life and ubiquitous computing. In this paper, we discussed a design of GRIB based ontology to enable smart weather information processing. GRIB is the general purposed and world-wildly used weather data format approved by the World Meteorological Organization. With the designed ontology and the inference system containing Jess engine, several intelligent weather applications have been implemented and tested to verify the virtue of machine-driven weather information processing.

• Journal of The Korean Association of Information Education
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• v.6 no.2
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• pp.234-245
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• 2002

A new era has come where high quality educational materials can be acquired easily through the World Wide Web. These materials, however, need to be refined and streamlined to maximize their effect on education. In order to provide such a streamlined flow, we need to be able to re-organize documents, which exist independent of each other on the Web, in a way that maintains their appropriate order in the right context to satisfy educational purposes. In addition, we should be able to provide supplementary explanations or missing information to the organized materials for smooth connections among them. In order to meet the requirements, we employed the virtual document concept that allows us to reuse existing documents for educational purposes. By providing a retrieval engine for virtual documents, we attempt to induce self-directed learning based on document retrieval, suitable for the level and purpose of students.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.816-822
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• 2016

Content-Based image retrieval is a method to search by image features such as local color, texture, and other image content information, which is different from conventional tag or labeled text-based searching. In real life data, the number of images having tags or labels is relatively small, so it is hard to search the relevant images with text-based approach. Existing image search method only based on image feature similarity has limited performance and does not ensure that the results are what the user expected. In this study, we propose and validate a machine learning based approach to improve the performance of the image search engine. We note that when users search relevant images with a query image, they would expect the retrieved images belong to the same category as that of the query. Image classification method is combined with the traditional image feature similarity method. The proposed method is extensively validated on a public PASCAL VOC dataset consisting of 11,530 images from 20 categories.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.39-42
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• 1998

음성인식기능을 이용한 지도정보 검색 시스템의 실용화를 위하여 독자적인 지도검색 알고리즘을 구현하여 기존의 GIS 용 검색툴을 이용함으로서 소요되는 비용을 최저화하면서도 어느 정도의 검색속도를 유지할 수 있는 음성구동지도검색시스템 구현을 위해 개발한 검색엔진의 성능 평가 실험을 통하여 그 유효성을 확인하고자 한다. 지도정보 검색시스템은 크게 음성인식부, 지도검색부로 나눌 수 있으며, 음성인식부에서는 유한상태오토마타에 의한 구문 제어를 통하여 OPDP 법으로 대상 단어의 인식을 수행하고, 지도검색부에서는 기존의 시스템에 사용된 OLE 기법에 기저한 Mapinfo 툴을 이용하지 않고, Visual C++를 이용한 독자적인 알고리즘을 구성하여 지도자료를 읽어 들이도록 구성하였다. 평가결과, 사무실 환경하에서 지도검색용 68단어를 대상으로 실시한 on-line test에서 검색 대상 단어인식률은 98.02%를 얻었으며, 이 때 해당지도를 화면에 나타내는데 걸리는 시간은 평균 18.2초가 소요되었다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.19-29
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• 2011

As people have heightened attention to blogs that are individual media, a variety rank algorithms was proposed for the blog search. These algorithms was modified for structural features of blogs that differ from typical web sites, and measured blogs' reputations or popularities based on the interaction results like links, comments or trackbacks and reflected in the search system. But actual blog search systems use not only blog-ranks but also search words, a time factor and so on. Nevertheless, those might not produce desirable results. In this paper, we suggest a topic-rank technique, which can find blogs that have significant degrees of association with topics. This technique is a method which ranks the relations between blogs and indexed words of blog posts as well as the topics representing blog posts. The blog rankings of correlations with search words are can be effectively computed in the blog retrieval by the proposed technique. After comparing precisions and coverage ratios of our blog retrieval system which applis our proposed topic-rank technique, we know that the performance of the blog retrieval system using topic-rank technique is more effective than others.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.209-215
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• 2005

Conventional ORDBMS vendors provide extension mechanisms for adding user-defined types and functions to their own DBMSs. Here, the extension mechanisms are implemented using a high-level interface. We call this technique loose-coupling. The advantage of loose-coupling is that it is easy to implement. However, it is not preferable for implementing new data types and operations in large databases when high Performance is required. In this paper, we propose to use the notion of tight-coupling to satisfy this requirement. In tight-coupling, new data types and operations are integrated into the core of the DBMS engine. Thus, they are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate information retrieval(IR) features and spatial database features into the Odysseus/IR ORDBMS that has been under development at KAIST/AITrc. In this paper, we introduce Odysseus/IR and explain its tightly-coupled IR features (U.S. patented). We then demonstrate a web search engine that is capable of managing 20 million web pages in a non-parallel configuration using Odysseus/IR.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.1009-1016
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• 2006

Under foundational precepts of Grid computing, two important requirements that all Grid application systems should satisfy are to accommodate the dynamic nature of Virtual Organizations (VOs), and to enforce different levels of security among different VOs. For the research described in this paper, we developed two different use-case scenarios addressing the two requirements, and then showed how the requirements can be met by implementing a Grid information retrieval (GIR) system prototype. The dynamic nature of VO applies not only to increasing and decreasing number of users, but also to the dynamically changing requirement of computing power among the different subcomponents that consist in overall system configuration. This implies that a request to increase computing power by a certain subcomponent can be satisfied by other idling subcomponents taking advantage of overall system flexibility. This paper describes how we implemented a Grid IR system using VO and security mechanisms provided by Globus toolkit 3.0, and shows how GIR system scalability and security can be improved for dynamic VOs. In order to manage different VOs, we implemented VO management service (VOMS), and registered it to Globus as an additional service.

• Annual Conference of KIPS
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• 2005.11a
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• pp.673-676
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• 2005

기존의 정보검색 엔진은 문서의 분야에 상관없이 본문 전체의 내용을 보여주므로 사용자가 적합한 내용인지를 파악하기 위해서는 본문 전체를 읽어 보아야 그 적절성 여부를 알 수 있다. 본 논문에서 제안하는 방법은 질의어가 지시하는 분야를 분야연상어를 이용하여 자동으로 파악하고, 사용자가 원하는 분야에서의 검색이 이루어지도록 하는 검색과 분류가 동시에 가능한 엔진을 설계하여 검색결과의 성능을 향상하고자 한다. 이와 함께 적당한 분야연상어가 다수 출현한 단락을 사용자에게 제공하여 본문 전체를 보지 않아도 질의어에 적당한 문서인지를 빠르게 파악하도록 설계하여 구현하였다.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.202-214
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• 2005

The semantic web is the web paradigm that represents not general link of documents but semantics and relation of document. In addition it enables software agents to understand semantics of documents. We propose a semantic search based on inference with ontologies, which has the following characteristics. First, our search engine enables retrieval using explicit ontologies to reason though a search keyword is different from that of documents. Second, although the concept of two ontologies does not match exactly, can be found out similar results from a rule based translator and ontological reasoning. Third, our approach enables search engine to increase accuracy and precision by using explicit ontologies to reason about meanings of documents rather than guessing meanings of documents just by keyword. Fourth, domain ontology enables users to use more detailed queries based on ontology-based automated query generator that has search area and accuracy similar to NLP. Fifth, it enables agents to do automated search not only documents with keyword but also user-preferable information and knowledge from ontologies. It can perform search more accurately than current retrieval systems which use query to databases or keyword matching. We demonstrate our system, which use ontologies and inference based on explicit ontologies, can perform better than keyword matching approach .