• International Journal of Contents
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• 제2권1호
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• pp.22-28
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• 2006

In this paper, we propose new semantic contents modeling using individual features, associative relations and visual features for efficiently supporting browsing and retrieval of video semantic contents. And we implement and design a browsing and retrieval system based on the semantic contents modeling. The browsing system supports annotation based information, keyframe based visual information, associative relations, and text based semantic information using a tree based browsing technique. The retrieval system supports text based retrieval, visual feature and associative relations according to the retrieval types of semantic contents.

• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• ETRI Journal
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• 제34권5호
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• pp.743-752
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• 2012

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

• International Journal of Internet, Broadcasting and Communication
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• 제8권4호
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• pp.63-72
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• 2016

Nominal compounds of 'N1 + N2'in Korean can be classified into the following three major categories: co-compound, sub-compound, and fusion. Among these three major categories, insertion of /t/ in the compounding process and subsequent tensification are found only in sub-compounds. This peculiar phenomenon of /t/-insertion which causes, in turn, tensification in sub-compounds has been long controversial because linguists have not been able to expect in which phonological environment of sub-compounding insertion of /t/ takes place. In this paper, I explore a phonological rule which makes it possible to expect the phonological environments of sub-compounding that allow insertion of /t/ and automatic tensification of the subsequent consonant in the onset of N2. In this process, I show that semantic feature(s) between two combined roots should be considered as one of the important structural descriptions in phonology.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.752-759
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• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.235-240
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• 2004

In this paper, we propose a framework for ontological knowledge-based image understanding systems. Ontology composed of concepts can be used as a guide for describing objects from a specific domain of interest and describing relations between objects from different domains The proposed framework consists of four main subparts ⅰ) ontological knowledge bases, ⅱ) primitive feature detectors, ⅲ) concept inference engine, and ⅳ) semantic inference engine. Using ontological knowledge bases on various domains and features extracted from the detectors, concept inference engine infers concepts on regions of interest in an image and semantic inference engine reasons semantic situations between concepts from different domains. We present a outline for ontological knowledge-based image understanding systems and application examples within specific domains such as text recognition and human recognition in order to show the validity of the proposed system.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권1_2호
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• pp.40-50
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• 2003

온톨로지(ontology)란 객체(object)들과 이들 사이의 관계의 정의에 의하여 어떤 사실이나 상태를 표하는 지식 표현 방법이다. 본 논문에서는 온톨로지를 이용한 웹 문서 분류와 이를 바탕으로 사용자의 정보 요구에 대한 개인화 된 정보를 제공하는 에이전트를 제안한다. 에이전트는 웹 문서들이 가지는 의미 구조를 계층적 형태로 표현한 온톨로지를 바탕으로 웹 문서를 분류하게 된다. 본 논문에서 온톨로지는 개념(concept)과 개념에 대한 특징(feature), 개념간의 관계(relation) 그리고 문서 분류를 위한 제약조건(constraint)으로 이루어진다. 에이전트는 사용자 프로파일과 문서 식별의 결과를 이용하여 사용자의 정보 요구를 효율적으로 파악하고 사용자의 브라우징을 돕게된다. 또한 에이전트는 선행탐색(look-ahead)방법을 통해 문서를 획득 문서를 개념으로 표현함으로써 사용자가 좀더 이해하기 쉬운 상위 단계의 윈 문서를 추천하게 된다.

• 한국지구과학회지
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• 제31권3호
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• pp.288-299
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• 2010

본 연구는 과학적 소양은 과학적 지식의 획득과 과학적 담화에 참여할 수 있는 언어적 능력을 통하여 길러진다는 전제하에 대기 중의 물의 상태변화에 관한 학생 글에서 나타나는 의미관계와 과학 언어적 특징을 알아보았다. 중학교 3학년 학생 67명이 참여하여 일상생활에서 흔히 경험할 수 있는 현상과 학교과학교육에서 체계적으로 배우는 현상 에 관한 두 개의 서술형 문항에 대한 글을 작성하였다. 연구의 결과 학생들은 '이슬점' 같은 생소한 용어뿐만 아니라 '수증기', '김' 등과 같은 친숙한 용어에 대해서도 잘못된 의미관계를 형성하고 있었고, 학교과학 교육보다 일상의 경험에서 형성된 지식에서 옳은 의미관계와 잘못된 의미관계 모두 더 많이 나타났다. 일상의 과학적 현상에 대해서는 행위와 절차를 중심으로 한 서술 양상이, 학교 교육에 의해 접하게 된 현상에 대해서는 전문용어와 명사구의 사용 양상이 보였다. 본 연구를 통하여 경험에 기초한 자발적 과정은 풍부한 의미관계 형성에, 형식적이고 이론적인 과정은 명사화를 중심으로 한 전문적이고 추상적인 서술의 측면에서 과학적 언어 능력 발달에 기여함을 알 수 있었다.