• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.219-233
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• 2008

This paper suggests an intelligent semantic blog model to systematically analyze and manage biogosphere with ontology as its conceptual knowledge base. In the model, the system managers may support users to easily find appropriate blog resources by tracking and analyzing various relationships between ontology - they may intelligently recommend Interest blog communities to relevant users by monitoring interaction activities in blogoshpere, dynamically grouping the communities with the ontology. To systematically specify the functionality of our model, 1) we first express the structure of blog resources in terms of objects and relationships between them and then 2) we formalize a set of operators designed to be applied to the resources. System queries are implemented by the combination of the operators.

• Journal of KIISE
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• v.43 no.3
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• pp.370-379
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• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.409-414
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• 2013

With the increase of multimedia information such as images, researches have been realized on how to extract the high-level semantic information from low-level visual information, and a variety of techniques have been proposed to generate this information automatically. However, most of these technologies extract the semantic information between single images, it's difficult to extract semantic information when a combination of multiple objects within the image. In this paper, we extract the visual features of objects within the image and training images stored in the DB and the features of each object are defined by measuring the similarity. Using ontology reasoner, each object feature within images infers the semantic information by positional relation and associative relation. With this, it's possible to infer semantic information between objects within images, we proposed a method for inferring more complicated and a variety of high-level semantic information.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.209-211
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• 2008

기술의 빠른 발전은 하드웨어와 소프트웨어의 다양한 결합방법으로 산업시스템을 정보기반 모델로 변경하고 있다. 엔지니어링시스템이 복잡해짐으로서 시스템의 에러를 줄이기 위한 다양한 시각화 모델 표현이 요구되고 연결관계를 구현하기위한 노력이 필요하다. 본 논문에서는 요구사항과 엔지니어링객체 사이에 추적고리로 디자인 컨셉을 삽입하여 외부 사용자 이벤트에 반응하고 추적고리를 설치하기 위한 시각적이고 효과적인 방법을 연구한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.275-278
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• 2012

웹 서비스는 SOA를 웹 상에서 구현한 기술로, 서비스들간의 느슨한 결합을 실현함으로써 재사용 가능한 서비스들의 활용을 극대화 한다. 본 논문에서는 확장 객체 기반 시소러스(eXtended Object-based Thesaurus, XOT)를 사용하여 적응 가능한 오케스트레이션 명세가 가능한 개념 기반 오케스트레이션 기법을 제안한다. XOT는 재사용 가능한 서비스들이 속하는 범주들을 개념으로 표현하고 상호 합성 패턴 관계를 포함한 개념간 상관 관계를 명시한 일종의 지식베이스이다. 이 기법은 오케스트레이션에 참여하는 복합 서비스를 포함한 여러 다양한 서비스들을 구체적으로 명세하는 대신 XOT 내의 개념들로 변수화 하고, 이들을 오케스트레이션 시 적절한 재사용 가능한 서비스 또는 구성 서비스들의 조합으로 대체시킴으로써 서비스들의 재사용성을 획기적으로 개선할 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.579-586
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• 2020

There is a large amount of multimedia data on the web page, and a method of extracting semantic information from low level visual information for accurate retrieval is being studied. However, most of these techniques extract one of information from a single image, so it is difficult to extract semantic information when multiple objects are combined in the image. In this paper, each low-level feature is extracted to extract various objects and backgrounds in an image, and these are divided into predefined backgrounds and objects using SVM. The objects and backgrounds divided in this way are constructed with ontology, infer the semantic information of location and association using inference engine. It's possible to extract the semantic information. We propose this method process the complex and high-level semantic information in image.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.789-796
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• 2006

Context-aware is the core in ubiquitous environment of sensor network to support intelligent and contextual adaptation service. The new context information model is demanded to support context-aware applications. The model should not depend on a specified application and be shareable between applications in the same environment. Also, it should support various context representation and complex context-aware. In this paper, we define the context information according to context-aware process. Also we design the knowledge of domain as well as applications using ontologies and rules. The domain spatial ontology and application knowledge are represented using the spatial object model and the rules of expanded ontologies, respectively. The expression of abundant spatial ontology represents the context information about distance between objects and adjacent object as well as the location of the object. The proposed context information model which is able to exhibit various spatial context and recognizes complex spatial context through the existing GIS. This model shows that it can adapt to a large scale outdoor context-aware applications such as air pollution and prevention of disasters as well as various context-aware applications.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.411-417
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• 2020

To utilize building information modeling (BIM) technology at the bridge maintenance stage, it is necessary to integrate large quantities of bridge inspection and model data for object-oriented information management. This research aims to establish the benefits of utilizing the extended industry foundation class (IFC)-BIM and ontology for bridge inspection information management. The IFC entities were extended to represent the bridge objects, and a method of generating the extended IFC-based information model was proposed. The bridge inspection ontology was also developed by extraction and classification of inspection concepts from the AASHTO standard. The classified concepts and their relationships were mapped to the ontology based on the semantic triples approach. Finally, the extended IFC-based BIM model was integrated with the ontology for bridge inspection data management. The effectiveness of the proposed framework for bridge inspection information management by integration of the extended IFC-BIM and ontology was tested and verified by extracting bridge inspection data via the SPARQL query.

• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.5-26
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• 2013

Although organizing standardized metadata is important for effective management of cultural heritage information, current metadata are represented differently according to the properties of the resources or objectives of the organizations in which they are accumulated. This research compared 6 different metadata formats and created 18 data elements for constructing databases. A religious architecture heritage information database was constructed based on 72 historic religious architectures, each composing of three parts. An ontology based on religious architecture heritage information was designed using a revised CIDOC-CRM, and was developed with a semi-automated corpus program.