• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.133-150
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• 2007

In this paper we investigate a trend analysis service using Semantic Web technology in a news domain. The trend analysis service can provide more intelligent answers rather than the answer given In current news search engines since it can analyze the passage of time and the relation among news. In order to provide the trend analysis service, the capability of temporal reasoning is required, but the Semantic Web language such as OWL does not support the reasoning capability. Therefore, we propose a language TL-OWL(Temporal Web Ontology Language) extending OWL with the temporal reasoning.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.239-246
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• 2010

This paper proposes a new document clustering method using NMF and fuzzy relationship. The proposed method can improve the quality of document clustering because the clustered documents by using fuzzy relation values between semantic features and terms to distinguish well dissimilar documents in clusters, the selected cluster label terms by using semantic features with NMF, which is used in document clustering, can represent an inherent structure of document set better. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• MALSORI
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• no.60
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• pp.97-108
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• 2006

The purpose of this study is to observe how Korean listeners detect a target phoneme with 'Focus' represented by prosodic prominence and question-induced semantic emphasis, and with intonational manipulation. According to the automated phoneme detection task using E-Prime, the Korean listeners detected phoneme targets more rapidly when the target-bearing words were in prominence position and in question-induced position. However, the presence of question-induced semantic emphasis reduced the prominence effect, so two effects interacted: when question-induced emphasis were primarily given as a cue, prominence which was given as secondary cue affected less to fine the new information. Besides, the intonation with manipulation was responded to faster than without manipulation.

• 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.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.34-46
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• 2023

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

• Journal of Korean Library and Information Science Society
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• v.36 no.3
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• pp.109-136
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• 2005

This paper describes the major components of ISO/IEC 11179 metadata registry (MDR) standard designed to promote data interoperability between systems, explains and discusses semantic web technology and Web ontology languages initiated by W3C that can be employed to further enhance data interoperability, and finally proposes a framework for a new RDF/OWL-based MDR to convert from the current human-readable MDR to machine-readable MDR. If the new MDR is successful, we might be able to offer a better customized information service to users. The future research will be concerned with evaluating objectively the effectiveness of machine-readable MDR in meeting the needs of real users.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.215-219
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• 2018

다중 화자 대화 속 인물 식별이란 여러 등장인물이 나오는 대본에서 '그녀', '아버지' 등 인물을 지칭하는 명사 또는 명사구가 실제 어떤 인물을 나타내는지 파악하는 문제이다. 대본 자연어 데이터만을 입력으로 하는 대화 속 인물 식별 문제는 드라마 대본에 대해서 데이터가 구축 되었고 이를 기반으로 여러 연구가 진행되었다. 그러나, 사람도 다중 화자 대화의 문장만 보고는 인물을 지칭하는 명사 또는 명사구가 실제 어떤 인물인지 파악하기 어려운 경우가 있다. 이에 본 논문에서는 발화가 되는 시점의 영상 장면 정보를 추가적으로 활용하여 인물 식별의 성능을 높이는 방법을 제시한다. 또한 기존 대화 속 인물 식별 연구들은 미리 정의된 인물을 대상으로 분류하는 형태로 접근해왔다. 이는 학습에 사용되지 않았던 인물이 나오는 임의의 다른 드라마 대본이나 대화 등에 바로 적용될 수 없다. 이에 본 논문에서는 영상 정보는 활용하되, 한번 학습하면 임의의 대본에 적용될 수 있도록 사전 인물 정보를 사용하지 않는 상호참조해결 기반의 인물 식별 방법도 제시한다.

• 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.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.349-364
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• 2013

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1195-1214
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• 2018

Typical search engines may not be the most efficient means of returning images in accordance with user requirements. With the help of semantic web technology, it is possible to search through images more precisely in any required domain, because the images are annotated according to a custom-built ontology. With appropriate annotations, a search can then, return images according to the context. This paper reports on the design of a tourism ontology relevant to touristic images. In particular, the image features and the meaning of the images are described using various properties, along with other types of information relevant to tourist attractions using the OWL language. The methodology used is described, commencing with building an image and tourism corpus, creating the ontology, and developing the search engine. The system was tested through a case study involving the western region of Thailand. The user can search specifying the specific class of image or they can use text-based searches. The results are ranked using weighted scores based on kinds of properties. The precision and recall of the prototype system was measured to show its efficiency. User satisfaction was also evaluated, was also performed and was found to be high.