• Spatial Information Research
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• v.20 no.2
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• pp.129-136
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• 2012

Recently international organizations such as ISO/TC211, OGC, INSPIRE (Infrastructure for Spatial Information in Europe) make an effort to share geospatial data using semantic web technologies. In addition, smart phone and social networking services enable community-based opportunities for participants to share issues of a social phenomenon based on geographic area, and many researchers try to find a method of extracting issues from that. However, serviceable spatial ontologies are still insufficient at application level, and studies of spatial information extraction from SNS were focused on user's location finding or geocoding by text mining. Therefore, a study of extracting spatial phenomenon from social media information and converting it into geosemantic knowledge is very usable. In this paper, we propose a framework for extracting keywords from micro-blog, one of the social media services, finding their relationships using data mining technique, and converting it into spatiotemopral knowledge. The result of this study could be used for implementing a related system as a procedure and ontology model for constructing geoseem antic issue. And from this, it is expected to improve the effectiveness of finding, publishing and analysing spatial issues.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.21-27
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• 2017

To guarantee the effectiveness of the HACCP(Hazard analysis and critical control points) system, it is necessary to develop of an ontology-based information system that can automatically manage the large amount of HACCP records or information derived from the HACCP operation results. In this paper, we construct a food information ontology which represents the relationships between ingredients, recipe, and features of food categories. Moreover, we develop HACCP automation application adopt the ontology to verify the semantic quality of the designed ontology model by performing HACCP processes such as HACCP diet classification. We expect to contribute to develop a food hygiene information and improve the accuracy of the HACCP data through the semantic system.

• 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 Geographical Society
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• v.48 no.1
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• pp.112-127
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• 2013

It is unsuitable for Korean coastal information to work specific tasks because the coastal information of the current provides simple information, and thus the coastal information is required to reprocess. Therefore, this paper intends to present the ontology model for managing the coastal information using Geo-Ontology and seek application of ontology. The contents of this paper follow as; First of all, I considered the base theories for ontology and related researches. Second, I built Geo-Ontology which defines taxonomy of geographical features and their relationships. Third, I designed and implemented the coastal information ontology about basin of coast, Masan, using Geo-Ontology. Fourth, I carried out semantic queries and reasoning, assessment of the coastal information ontology. This paper will be a base study for many projects which are currently being conducted to integrate spatial information for more effective administrative works and easier maintenance and management of data. Also, this paper is significant in the sense that it is the study preparing for linked data.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.35-45
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• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.166-172
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• 2014

Image classification is an important problem in computer vision. However, it is a very challenging problem due to the variability, ambiguity and scale change that exists in images. In this paper, we propose a method of a hypergraph based modeling can consider the higher-order relationships of semantic attributes of a scene image and apply it to a scene image classification. In order to generate the hypergraph optimized for specific scene category, we propose a novel search method based on a probabilistic subspace method and also propose a method to aggregate the expression values of the member semantic attributes that belongs to the searched subsets based on a linear transformation method via likelihood based estimation. To verify the superiority of the proposed method, we showed that the discrimination power of the feature vector generated by the proposed method is better than existing methods through experiments. And also, in a scene classification experiment, the proposed method shows a competitive classification performance compared with the conventional methods.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.31-40
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• 2012

Ubiquitous technology influences on various firms of contents needed for self-healthcare, as it fuses into medical services. Particularly, rapid changes in the web and mobile environment, requiring various sorts of healthcare and its related contents, make efficiency of search more important. Personalized contents needs to be more refined as well as the existing simple keyword-centered searching method needs to be more effective in order to meet both requirements and characteristics of each patient or each user. A precise semantic searching method is required for a system to understand promptly the meaning of a contents. In this respect, to build a healthcare ontology has its own significance. This study builds up a system model that can be utilized practically in existing systems by setting up the Food Class and its sub-class among the healthcare contents with Protege tool and then materializing constraints and its relationships between each class. The healthcare contents ontology provides patients or users with a platform which can search the needed information promptly and precisely.

• The Journal of Korean Association of Computer Education
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• v.12 no.5
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• pp.95-103
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• 2009

In this paper, we propose a Web navigation guidance system which automatically provides a user with semantically related links based on an ontology. The system associates each web page to a concept in the ontology and creates new links between web pages by considering relationships of the concepts defined in the ontology. It focuses on enhancing web navigation by offering semantic links based on an ontology. We experimented the proposed system with 5th grade students who were performing tasks by searching Web pages and found that the degree of disorientation, the ratio of revisits for Web pages, and time spent for completing tasks for students in the experimental group were smaller than those for students in the control group. In addition, the task performance ratio for students in the experimental group were higher than that for students in the control group. It is expected that the proposed system can help design a navigable web site that is important in Web-based education.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.45-52
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• 2012

This paper proposes a new enhancing document clustering method using the important terms of cluster and the wikipedia. The proposed method can well represent the concept of cluster topics by means of selecting the important terms in cluster by the semantic features of NMF. It can solve the problem of "bags of words" to be not considered the meaningful relationships between documents and clusters, which expands the important terms of cluster by using of the synonyms of wikipedia. Also, it can improve the quality of document clustering which uses the expanded cluster important terms to refine the initial cluster by re-clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Journal of Korean Elementary Science Education
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• v.39 no.3
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• pp.399-411
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• 2020

This study aims to explore how elementary students perceive and approach the issue of plastic debris in marine habitats by examining students' perspectives on the ecosystem and environmental solutions. The study was conducted to 143 Grade Four elementary school students in Seoul. After implementing two class-units on plastic waste, students' constructed responses on the problem of and solutions to plastic debris in marine habitats were collected. Data were analyzed through semantic network analysis and the keywords were visualized to reflect their relationships. Furthermore, students' responses on how they perceive environmental problems were further analyzed based on the following analysis criteria: students' perspectives on the ecosystem, the level of complexity of food chain(s), and the scope of their perspective. Also, student responses on environmental solutions were classified to be either at a personal or social level. Through semantic network analysis, keywords identified for students' perceptions on the problem were the sea, plastic, debris, animals, living things, humans, extinction, while keywords extracted for the solutions were plastic, debris, recycling, disposable, and I. Based on the analysis criteria, it was found that students were well aware of the food chain concept, could perceive the ecosystem as having comprised of both biotic and abiotic factors, and could approach the problem beyond the scope of the marine environment. Also, most students mentioned the solutions only at a personal level. Based on the findings, implications on how to move forward in educating environmental issues related to the ecosystem in science education is further discussed.