• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.248-250
• /
• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.6 no.4
• /
• pp.309-314
• /
• 2005

This paper analyzes techniques that extract objective information from distributed web databases for bioinformatics based on relationship among information. Moreover, we discuss the design and implementation of a method for knowledge enhancement in respect of protein information. Web data extractor can be constructed by using a manual, semi-automatic, or automatic way. Data extractor generally makes use of identifiers in order to search and extract targeting information from a specified web page. This paper presents a design and implementation for the protein databases of an organism by utilizing web data extraction techniques.

• Journal of dental hygiene science
• /
• v.9 no.1
• /
• pp.17-23
• /
• 2009

A survey using a questionnaire was conducted among industrial employees in Ulsan Total surveyed persons was 244. The purpose of this study was to analysis the relation and behavior of oral health promotion. Also, This study was to provide educational data of oral health. The obtained were analysed by SPSS program 12.0. The findings were as below: 1. Dental visit for prevention Y/N(%) was higher 50 years than 20 years(p < .001). Oral hygiene devices Y/N(%) for oral hygiene showed University graduates higher than Middle school graduates respectively(p < .05). 2. The knowledge of oral health was higher High school graduates than Middle school graduates respectively(p < .05). The rate of brushing teeth 3~5 times per day was higher females than males(p < .01) and better College graduates than Middle school graduates respectively(p < .001). 3. Oral health conditions of respondents were better 20years and 30years than 50years respectively(p < .001). Self-evaluation of Oral health sensitive was better College graduates than Middle school graduates respectively(p < .05). 4. Correlation between of Tooth brushing Frequency per Day by Oral health knowledge, dental clinic Visit, Extraction number had an effect on the significant dental clinic visit for prevention and the knowledge of Oral health had even a positive effect relationship(r = .233, p = .001). The knowledge of Oral health and the frequency of tooth brushing had even a positive effect relationship(r = .161, p = .05). The frequency of tooth brushing and the number of extraction of teeth had a negative effect relationship(r = -.145, p = .05).

• Journal of Korea Spatial Information System Society
• /
• v.1 no.1 s.1
• /
• pp.5-18
• /
• 1999

Recently, the necessity of automatic knowledge extraction from spatial data stored in spatial databases has been increased. Spatial data mining can be defined as the extraction of implicit knowledge, spatial relationships, or other knowledge not explicitly stored in spatial databases. In order to extract useful knowledge from spatial data, an object management system that can store spatial data efficiently, provide very fast indexing & searching mechanisms, and support a distributed computing environment is needed. In this paper, we designed and implemented an open object management system for spatial data mining, that supports efficient management of spatial, aspatial, and knowledge data. In order to develop this system, we used Open OODB that is a widely used object management system. However, the lark of facilities for spatial data mining in Open OODB, we extended it to support spatial data type, dynamic class generation, object-oriented inheritance, spatial index, spatial operations, etc. In addition, for further increasement of interoperability with other spatial database management systems or data mining systems, we adopted international standards such as ODMG 2.0 for data modeling, SDTS(Spatial Data Transfer Standard) for modeling and exchanging spatial data, and OpenGIS Simple Features Specification for CORBA for connecting clients and servers efficiently.

• Journal of Digital Convergence
• /
• v.10 no.11
• /
• pp.427-432
• /
• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Journal of the Korean Institute of Gas
• /
• v.25 no.3
• /
• pp.9-15
• /
• 2021

In this paper, a study on the construction of a knowledge base based on natural language processing and the design of a chemical substance estimation system for the development of a knowledge service for a real-time sensor information fusion detection system and symptoms of contact with chemical substances in industrial sites. The information on 499 chemical substances contact symptoms from the Wireless Information System for Emergency Responders(WISER) program provided by the National Institutes of Health(NIH) in the United States was used as a reference. AllegroGraph 7.0.1 was used, input triples are Cas No., Synonyms, Symptom, SMILES, InChl, and Formula. As a result of establishing the knowledge base, it was confirmed that 39 symptoms based on ammonia (CAS No: 7664-41-7) were the same as those of the WISER program. Through this, a method of establishing was proposed knowledge base for the symptom extraction process of the chemical substance estimation system.

• ETRI Journal
• /
• v.32 no.5
• /
• pp.676-686
• /
• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.1136-1141
• /
• 2003

Due to the lack of digitally usable standards, it has been known to be difficult to handle the biological data. For example, the name of genes and proteins changes over time or has several synonyms indicating different entities. To cope with these problems, several communities, including the Gene Ontology Consortium and PubGene are making their efforts to move science toward the semantic web vision. Although some progress has been made, its expressivity is not sufficient for full-fledged ontological modeling and reasoning. This paper suggests a methodology for representing and extracting biological knowledge by using Web Ontology Language (OWL) as an extension of Resource Description Framework Schema (RDFS). Some benefits of our approach are: (1) to ensure extended sharing of biological meta data on the Web, and (2) to enrich additional expressivity and the semantics of RDFS+OWL.

• Proceedings of the Korean Society for Language and Information Conference
• /
• 2007.11a
• /
• pp.508-514
• /
• 2007

In order to extract some important information of a person from text, an extracting model was proposed. The person's name is recognized based on the maximal entropy statistic model and the training corpus. The sentences surrounding the person's name are analyzed according to the conceptual knowledge base. The three main elements of events, domain, situation and background, are also extracted from the sentences to construct the structure of events about the person.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.11b
• /
• pp.88-90
• /
• 2005

최근 인터넷의 급속한 성장은 이미지와 같은 멀티미디어 정보의 급격한 증가를 가져왔다. 따라서 사용자로 하여금 원하는 이미지를 검색하는데 있어서 좀 더 효율적이고 정확한 검색 방법의 필요성이 대두되어 왔다. 일반적으로 이미지 검색 방법에는 키워드 기반 방식과 내용 기반 방식이 존재한다. 그러나 위 두 방법은 지금의 대용량 이미지 데이터베이스 검색에 있어서 여러 문제점들을 가지고 있다. 특히, 키워드 기반 방식을 보완하기 위해서 제안되어진 내용 기반 방식의 경우, 사람이 인식할 수 있는 의미 정보가 아닌 시각 정보만을 이용하기 때문에 시맨틱 갭(semantic gap) 문제가 발생하게 된다. 본 논문에서는 이미지 객체의 시각 정보들에 대한 중간 의미값으로 구성된 시각 정보 온톨로지와 동물에 대한 분류 정보를 표현하고 있는 동물 온톨로지를 구축하고, 이를 이용하여 이미지로부터 .고수준의 의미 정보를 완전 자동으로 추출하는 효율적인 방법을 제안한다.