• 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 KIISE:Computing Practices and Letters
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• v.13 no.1
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• pp.24-34
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• 2007

Since Context Driven Component model is based on the idea that a context-aware application consists of the components that are context sensitive and the components that do not depend on the context, it divides the context sensitive part into components according to which context information they are related to. The model supports the scalability of context information by building an application through composing Context Driven Components. Furthermore, it solves the embeddedness of context information inside the application logic. To show the contributions of the model, this paper applies it to Call-forwarding application, and analyses how the model supports the scalability and the embeddedness.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.29-47
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• 2007

Acquiring context data in a timely and correct way is now regarded as one of the crucial characteristics of the proactive service which runs on ubiquitous computing environment. Moreover, context model should be well designed to provide a solid context-aware system. Since the ubiquitous computing systems aim to provide context-aware services everywhere with any available devices, legacy services which uses context models assuming single or limited domain should be extended enough to be useful even for multi-domain muli-services. This leads us to a motivation to build a generic context model with an appropriate type of model. Hence, the purpose of this paper is to propose a generic context model by assessing a variety of model types with a sort of evaluation measures.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.239-248
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• 2006

Current context-aware applications In ubiquitous computing environments make the assumption that the context they are dealing with is correct. However, in reality, both sensed and interpreted context informations are often uncertain or imperfect. In this paper, we propose a probability extension model to ontology-based model for rep resenting uncertain contexts and use Bayesian networks to resolve about uncertainty of context informations. The proposed model can support the development and operation of various context-aware services, which are required in the ubiquitous computing environment.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1250-1260
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• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• Journal of Korean Library and Information Science Society
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• v.43 no.1
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• pp.221-239
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• 2012

The information that has been created according to the complex environment and usage pattern of library user can provide context-aware information service through knowledge structuralization on whether it is a suitable situation for user. Accordingly, the development of a context model for defining the various contexts of library user and for the structuralization of interrelated context information is an essential requirement. This study examined the context concept and context modeling, and utilizing the concept of Activity Theory by Engestrom, the activity model of library user was designed as 1) subject, 2) object, 3) tools, 4) divison of labor, 5) community, and 6) rules. In addition, for the purpose of analyzing the context of library user, activity information was tracked to utilize the Shadow Tracking for observing and recording their forms, and the methodology of CAbAT (Context Analysis based on Activity Theory) was utilized for the collected activity information to analyze the user context model.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.22-30
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• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.387-392
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• 2016

Context-aware systems require sensor data collecting model and context representing model to provide user-demand services. Sensor data collecting model consists of sensor access information, sensor value, and definition of value types. Context representing model involves certain keywords to symbolize environmental information including the field from sensor data collecting model that is described in markup language such as XML. However, duplicated keywords could be assigned to different contextual information by service developers. As a result, the system may cause misunderstanding and misleading wrong situational information from unintended contextual information. In this paper, we propose a context classification model for collecting appropriate access information and defining the specification of context.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.225-232
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• 2009

Context-aware technologies for ubiquitous computing are necessary to study the representation of gathered context-information appropriately, the understanding of user's intention using context-information, and the offer of pertinent services for users. In this paper, we propose the WS-CAM(Web Services based Context-Aware Middleware) framework for context-aware computing. WS-CAM provides ample power of expression and inference mechanisms to various context-information using an ontology-based context model. We also consider that WS-CAM is the middleware-independent structure to adopt web services with characteristic of loosely coupling as a matter of communication of context-information. In this paper, we describe a scenario for lecture services based on the ubiquitous computing e e e e e e to verify the utilization of WS-CAM We also show an example of middleware-independent system expansion to display the merits of web-based services. WS-CAM for lecture services represented context-information itodomaits as OWL-based ontology model effectively, and we confirmed the information is inferred to high level context-information by user-defined rules. We also confirmed the context-information is transferred to application services middleware-independently using various web methods provided by web services.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1378-1391
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• 2019

In recent years, automatic document summarization has been widely studied in the field of natural language processing thanks to the remarkable developments made using deep learning models. To decode a word, existing models for abstractive summarization usually represent the context of a document using the weighted hidden states of each input word when they decode it. Because the weights change at each decoding step, these weights reflect only the local context of a document. Therefore, it is difficult to generate a summary that reflects the overall context of a document. To solve this problem, we introduce the notion of a general context and propose a model for summarization based on it. The general context reflects overall context of the document that is independent of each decoding step. Experimental results using the CNN/Daily Mail dataset show that the proposed model outperforms existing models.