• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.559-569
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• 2003

Annotation is used for inscribing personal opinion, explanation, and summary. Various methods for processing annotation efficiently in digital document environments are being studied. However, previous studies placed much emphasis on function of annotation, so either they did not support Intuitive paper-based input interface or the systems that support it still have low reusability problems, because relation between annotation and original document are not explicit. Thus, in our study, we define context-based annotation modeling for digital document environments, and suggest annotation interface based on the modeling. To design annotation model, we define annotation types, context information of document, and relationship between annotation and original document. Also, a system based on the modeling is implemented to support pen-based annotation and annotation DTD. As a result, unlike previous studies, it is possible to explicitly define context-based annotation in pen-based input environments. We present various functions using the modeling and various possibilities of application.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.607-610
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• 2001

기존의 전자책 환경에서 종이책과 구별되는 특징은 네트웍을 통한 저자와 독자, 출판사간의 인터랙티브(interactive)한 정보 교환이 가능하다는 점이다. 이러한 교환은 기존의 종이책에서 사용하는 Annotation을 이용하면 가능하다. Annotation이란 원본 문서에 부가적으로 추가되는 정보를 의미한다. 그러므로 Annotation과 원본 문서는 밀접한 관계를 갖는데 기존의 Annotation 모델링은 원본 문서를 고려하지 않고 Annotation만을 별개로 모델링하였다. 이에 본 논문에서는 Annotation을 보다 효과적으로 활용하기 위하여 annotation과 원본 문서를 동시에 표현하는 모델링에 대해 제안한다. 그리고 본 모델링은 전자책 표준인 EBKS에 기반하며, 모델링 결과를 웹 자원을 기술하는 표준인 RDF를 이용하여 표현한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.602-606
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• 2001

전자책은 기존 종이 문서에 비하여 다양한 기능 및 장점을 제공할 수 있기 때문에 현재 다양한 연구 및 서비스가 제공되고 있다. 또한 전자책 환경에서의 정보 공유 및 검색과 같은 다양한 활용을 위해서는 반드시 Annotation 지원이 가능하여야 하며 이에 대한 정확한 Annotation 정의가 요구된다. Annotation이란 일반적으로 문서의 주제 및 내용에 관한 해설, 설명, 그리고 강조를 목적으로 추가되는 문장 또는 텍스트를 의미한다. 그러나 기존 전자책 환경에서의 Annotation과 관련된 연구에서는 이에 대한 심도있는 연구 결과가 미비한 실정이다. 이에 본 연구에서는 전자책 환경을 위한 Context 기반 Annotation Modeling을 정의하고 이를 활용한 인터페이스를 제안한다. 현재 전자책 환경은 대부분 XML에 기반하고 있으며 이에 본 논문에서는 구조정보와 컨텐츠, 그리고 Annotation간의 관계 및 이를 활용하기 위한 모델링을 제시한다. 또한 모델링 정보를 이용한 다양한 장점 및 환용이 가능한 시스템을 구현하였다. 그 결과 본 연구에서는 기존 연구와는 달리 Context 기반 Annotation의 정확한 정의가 가능하며 이를 활용한 다양한 기능을 제공하는 동시에 앞으로의 응용 가능성을 제시하고 있다.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.565-575
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• 2003

This paper proposed context based annotation model and annotation ambiguity correction methods. The proposed model provides various annotation types, semantic models, and pen-based free drawing interface. Annotation correction method that is specifically based on the context which includes various textual and structure information between free-form marking and annotation. Also, interface for XML environment using the proposed model and correction methods is proposed and possibilities of application is looked at. The results from the implementation of the proposed method show that the annotated areas included in the free-form marking information are more accurate, achieving more accurate exchange results amongst multiple users in a heterogeneous document environment

• Journal of The Korean Association of Information Education
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• v.10 no.2
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• pp.219-226
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• 2006

For the accurate creation of annotation information in a free-form annotation environment, the ambiguity that arises in the analysis stage between the geometric information and annotations needs to be resolved. Therefore, this This paper identifies, analyzes, and proposes presents solutions methods for the ambiguity that can occur between free-form marking and various contexts in XML-based annotation environment. The proposed method is based on context which includes various textual and structure information between free-form marking and annotated part. The proposed method show that the annotated portions areas included in the free-form marking information are more accurate, achieving more accurate exchange results amongst multiple users in a heterogeneous document environment. This study can be effectively applied to eLearning, Cyber-Class, and IETM

• Journal of KIBIM
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• v.13 no.4
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• pp.85-95
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• 2023

In the process of creating drawings based on Building Information Modeling (BIM), automatically generated annotations can cause interference issues depending on the drawing type. This study aims to develop an algorithm for repositioning annotations using genetic algorithms to minimize such interferences. To achieve this, the Application Programming Interface (API) of BIM software was used to analyze data extractable from BIM drawing files. The process involved defining drawing data related to annotation repositioning, preprocessing this data, and deriving optimal placement coordinates for the annotations. Furthermore, applying the developed algorithm to the preliminary design drawings of small and medium-sized neighborhood facilities resulted in approximately a 95.37% decrease in annotation interference, indicating that the proposed algorithm can significantly enhance productivity in BIM-based drawing tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.548-557
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• 2010

In this paper, we propose a semi-automatic modeling approach of ontology to annotate VOD to realize the IPTV's intelligent searching. The ontology is made by combining partial tree that extracts hypernym, hyponym, and synonym of keywords related to a service domain from WordNet. Further, we add to the partial tree new keywords that are undefined in WordNet, such as foreign words and words written in Chinese characters. The ontology consists of two parts: generic hierarchy and specific hierarchy. The former is the semantic model of vocabularies such as keywords and contents of keywords. They are defined as classes including property restrictions in the ontology. The latter is generated using the reasoning technique by inferring contents of keywords based on the generic hierarchy. An annotation generates metadata (i.e., contents and genre) of VOD based on the specific hierarchy. The generic hierarchy can be applied to other domains, and the specific hierarchy helps modeling the ontology to fit the service domain. This approach is proved as good to generate metadata independent of any specific domain. As a result, the proposed method produced around 82% precision with 2,400 VOD annotation test data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.392-412
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• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• Genomics & Informatics
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• v.19 no.4
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• pp.43.1-43.12
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• 2021

Campylobacter jejuni is one of the most prevalent organisms associated with foodborne illness across the globe causing campylobacteriosis and gastritis. Many proteins of C. jejuni are still unidentified. The purpose of this study was to determine the structure and function of a non-annotated hypothetical protein (HP) from C. jejuni. A number of properties like physiochemical characteristics, 3D structure, and functional annotation of the HP (accession No. CAG2129885.1) were predicted using various bioinformatics tools followed by further validation and quality assessment. Moreover, the protein-protein interactions and active site were obtained from the STRING and CASTp server, respectively. The hypothesized protein possesses various characteristics including an acidic pH, thermal stability, water solubility, and cytoplasmic distribution. While alpha-helix and random coil structures are the most prominent structural components of this protein, most of it is formed of helices and coils. Along with expected quality, the 3D model has been found to be novel. This study has identified the potential role of the HP in 2-methylcitric acid cycle and propionate catabolism. Furthermore, protein-protein interactions revealed several significant functional partners. The in-silico characterization of this protein will assist to understand its molecular mechanism of action better. The methodology of this study would also serve as the basis for additional research into proteomic and genomic data for functional potential identification.