• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.461-472
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• 2005

In this paper, we design and implement a graphical bio-ontology management system based on OWL(Web Ontology Language). It allows domain experts to easily manage sophisticated bio-ontologies in which biological knowledge is encoded. The knowledge can be seamlessly modeled into the ontology by well defined graphical notations, which capture most of subtle semantics inherently existing between biological terms. Our system provides a new construction mechanism, which can determine a considerable part of relationships between terms by their inheritance and inverse-inheritance. For keeping their semantics to be consistent, the mechanism supplies domain experts with information available from relationships being constructed or already constructed. The constructed ontology is basically formatted by OWL, which may benefit from its powerful semantic expressiveness. Additionally, it can be automatically translated into other standard languages without semantic loss, such as RDF/RDFS, DAML+OIL and so on. The main characteristics of our system is that it enables domain experts to delicately model the bio-ontology by the visualized construction mechanisms adopting well-defined graphical notations based on OWL.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.159-163
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• 2006

최근 도메인에 기반한 개념과 이들 개념간의 관계를 설정할 수 있는 온톨로지 기반 컨텍스트 모델들이 유비쿼터스 컴퓨팅 환경에서 연구되고 있다. 그러나 새로운 개념의 추가에 따른 온톨로지 재구성은 시간이나 비용적으로 많은 오버헤드를 초래한다. 즉 하위 레벨에 있는 임의의 데이터 구조가 수정될 때마다 계속 적으로 기존 온톨로지를 변경해야만 한다. 따라서 센서 노드의 동적인 컨텍스트 표현보다 미들웨어와 같은 최상위의 추상적이고 정적인 컨텍스트 표현에 적합하다. 한편 센서 네트워크에서의 베이스 스테이션 기반 상황인지 기법은 상황 조건을 만족시키지 못하는 저수준 센싱 정보를 베이스 스테이션으로 전송하는데 많은 에너지를 소모한다. 본 논문에서는 센서 노드에서의 동적인 컨텍스트의 표현과 저수준 센싱 정보의 전송량을 줄이기 위해 메타데이터와 RDF 기반 구조의 변형인 Triple 구조를 결합한 컨텍스트 모델은 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.315-319
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• 2008

인터넷 환경은 놀랄만한 속도로 발전하고 있다. 사용자의 수가 늘어나고 그와 동시에 자료의 양도 폭발적으로 늘어나고 있음에 따라, 정확한 정보를 찾고 불필요한 정보를 필터링 하는 기술이 요구되기 시작하였다. 그 대표적인 기술이 시맨틱 웹(Semantic Web)[1] 으로 시맨틱 웹은 웹상의 정보에 메타데이터를 추가로 정의하여 사람 뿐 아니라 컴퓨터가 그 정보의 의미를 파악 할 수 있도록 하는 것이다. 이러한 시맨틱 웹을 위해서는 기계가 의미를 이해할 수 있도록 온톨로지(Ontology)를 먼저 구축해야만 한다. 온톨로지는 자원과 개념의 관계를 정의해 놓은 일종의 사전으로 이를 기술하기 위한 언어로는 RDF, OWL등이 있다. 온톨로지 데이터가 증가함에 따라 온톨로지 저장소의 크기가 증가하게 되면 성능을 위해 지역적으로 온톨로지 저장소를 분산해야 한다. 이에 따라 본고는 분산 환경에서의 통합된 질의에 대한 연구를 바탕으로 확장 가능하고 유연한 구조의 분산 온톨로지 모델을 제시한다.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1498-1504
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• 2016

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.261-276
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• 2020

Digital contents services are one of the topics that have been intensively studied in the media industry, where various semantic and ontology techniques are applied. However, query execution for ontology data is still inefficient, lack of sufficient extensible definitions for node relationships, and there is no specific semantic method fit for media data representation. In order to make the machine understand digital contents (DCs) data well, we analyze DCs data, including static data and dynamic data, and use ontology to specify and classify objects and the events of the particular objects. Then the formal representation method is proposed which not only redefines DCs data based on the technology of OWL/RDF, but is also combined with media segmentation methods. At the same time, to speed up the access mechanism of DCs data stored under the persistent database, an ontology-based DCs query solution is proposed, which uses the specified distance vector associated to a surveillance of semantic label (annotation) to detect and track a moving or static object.

• Journal of the Korean Society for information Management
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• v.20 no.1
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• pp.341-371
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• 2003

The purpose of the study is to design the ontology-based Prescription Knowledge Management System of Oriental Medicine. The study was done with the premise that the effectiveness of the system can be improved by using ontological abstractional structure based on definition of concept, attribution and relations of words related to the specific area. The system is developed with Protege-2000 by using newly developed KPML(Korean Medicine Prescription Markup Language). The results of the study provide the model of Prescription Knowledge Management System and the possibility of implementing XML-based ontology system to the semantic web environment.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1585-1600
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• 2009

The goal of Semantic Web is to provide efficient and effective semantic search and web services based on the machine-processable semantic information of web resources. Therefore, the process of creating and adding computer-understandable metadata for a variety of web contents, namely, semantic annotation is one of the fundamental technologies for the semantic web. Recently, in order to manage annotation metadata, direct approach for embedding metadata into the document is mainly used in semantic annotation. However, many semantic annotation tools for web documents have been mainly worked with HTML documents, and most of these tools do not support semantic search functionalities using the metadata. In this paper, based on these problems and previous works, we propose the Ontology-based Semantic Annotation tool(OSA) to efficiently support semantic annotation for web documents(such as HTML, PDF). We define a semantic annotation model that represents ontological-semantic information by using RDFS(RDF Schema). Based on XMP(eXtensible Metadata Platform) standard, the model is encoded directly into the document. By using OSA with XMP, user can perform semantic annotation on web documents which are able to keep compatibility for managing annotation metadata. Eventually, the integrated semantic annotation metadata can be used effectively in semantic search for a variety of web contents.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.43-66
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• 2008

Currently be proceeded a lot of researchers for 'user information demand description' for interface of an information retrieval system or Web search engines, but user information demand description for a natural language form is a difficult situation. These reasons are as they cannot provide the semantic similarity that an information retrieval model can be completely satisfied with variety regarding an information demand expression and semantic relevance for user information description. Therefore, this study using the description logic that is a knowledge representation base of OWL and a vector model-based weight between concept, and to be able to satisfy variety regarding an information demand expression and semantic relevance proposes a decision way for perfect assistances of user information demand description. The experiment results by proposed method, semantic similarity of a polyseme and a synonym showed with excellent performance in decision.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.