• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.13-28
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• 2007

This is a speculative paper, describing a recently started effort to give a formal semantics to semantic annotation schemes. Semantic annotations are intended to capture certain semantic information in a text, which means that it only makes sense to use semantic annotations if these have a well-defined semantics. In practice, however, semantic annotation schemes are used that lack any formal semantics. In this paper we outline how existing approaches to the annotation of temporal information, semantic roles, and reference relations can be integrated in a single XML-based format and can be given a formal semantics by translating them into second-order logic. This is argued to offer an incremental aproach to the incorporation of semantic information in natural language processing that does not suffer from the problems of ambiguity and lack of robustness that are common to traditional approaches to computational semantics.

• 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.

• Genomics & Informatics
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• v.18 no.2
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• pp.19.1-19.6
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• 2020

In semantic annotation, semantic concepts are linked to natural language. Semantic annotation helps in boosting the ability to search and access resources and can be used in information retrieval systems to augment the queries from the user. In the research described in this paper, we aimed to identify ontological concepts in scientific text contained in spreadsheets. We developed a tool that can handle various types of spreadsheets. Furthermore, we used the NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionality to cover spreadsheet data. Table2Annotation has strengths in certain criteria such as speed, error handling, and complex concept matching.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.548-556
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• 2007

We present an automatic semantic annotation system for Korean on the EXCOM (EXploration COntextual for Multilingual) platform. The purpose of natural language processing is enabling computers to understand human language, so that they can perform more sophisticated tasks. Accordingly, current research concentrates more and more on extracting semantic information. The realization of semantic processing requires the widespread annotation of documents. However, compared to that of inflectional languages, the technology in agglutinative language processing such as Korean still has shortcomings. EXCOM identifies semantic information in Korean text using our new method, the Contextual Exploration Method. Our initial system properly annotates approximately 88% of standard Korean sentences, and this annotation rate holds across text domains.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.3-8
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• 2015

최근에 ISO/TC 37/SC 4 산하의 Working Group 2 Semantic annotation에서 자연언어의 의미주석에 관한 4 개의 국제표준을 완성하여 출판하였다. 그 중에서 2 개의 국제표준 ISO 24617-1 SemAF-Time(ISO-TimeML)[1]과 24617-7 ISOspace[2]를 간략히 소개하는 것이 이 발표의 목적이다. 자연언어 텍스트에서 전자는 사건과 관련된 시간 정보를 주석처리하고, 후자는 사건(event), 특히 운동(motion)과 관련된 공간 정보를 주석 처리하는 주석체계(annotation scheme)들을 구축, 기술하는 명세언어(specification language)이다. 이 표준들은 또한 ISO 24612:2012 LAF (Linguistic annotation framework)[3]의 제약조건들을 준수하며 언어 주석체계를 구축하였다. 오늘의 발표는 이들 두 개의 국제표준에 준한 주석체계들 ASisoTime과 ASisoSpace가 LAF를 따라 어떻게 구축되었는지 그 추상통사구조(abstract syntax)를 명시하고, 의미주석체계로서의 이들 주석체계의 타당성을 보이기 위하여 주석기반의 의미형식(semantic form)들을 체계적으로 도출하는 과정을 또한 보이도록 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4476-4490
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• 2017

The aim of image annotation is to determine labels that can accurately describe the semantic information of images. Many approaches have been proposed to automate the image annotation task while achieving good performance. However, in most cases, the semantic similarities of images are ignored. Towards this end, we propose a novel Visual-Semantic Nearest Neighbor (VS-KNN) method by collectively exploring visual and semantic similarities for image annotation. First, for each label, visual nearest neighbors of a given test image are constructed from training images associated with this label. Second, each neighboring subset is determined by mining the semantic similarity and the visual similarity. Finally, the relevance between the images and labels is determined based on maximum a posteriori estimation. Extensive experiments were conducted using three widely used image datasets. The experimental results show the effectiveness of the proposed method in comparison with state-of-the-arts methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.158-165
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• 2018

Since a large amount of multimedia image has dramatically increased, it is important to search semantically relevant image. Thus, several semantic image annotation methods using RDF(Resource Description Framework) model in mobile environment are introduced. Earlier studies on annotating image semantically focused on both the image tag and the context-aware information such as temporal and spatial data. However, in order to fully express their semantics of image, we need more annotations which are described in RDF model. In this paper, we propose an annotation method inferencing with RDFS entailment rules and user defined rules. Our approach implemented in Moment system shows that it can more fully represent the semantics of image with more annotation triples.

• 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 Digital Contents Society
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• v.12 no.4
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• pp.429-436
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• 2011

Various documents, images, videos and other materials on the web has been increasing rapidly. Efficient search of those things has become an important topic. From keyword-based search, internet search has been transformed to semantic search which finds the implications and the relations between data elements. Many annotation processing systems manipulating the metadata for semantic search have been proposed. However, annotation data generated by different methods and forms are difficult to process integrated search between those systems. In this study, in order to resolve this problem, we categorized levels of many annotation documents, and we proposed the method to measure the similarity between the annotation documents. Similarity measure between annotation documents can be used for searching similar or related documents, images, and videos regardless of the forms of the source data.