• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.87-88
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• 2008

As an information service system based on semantic web technology, $OntoFrame^{(R)}$ takes aim at a framework for providing analysis and fusion services of academic information. It currently consists of three parts: ontologies representing knowledge schema derived from academic information, $OntoURI^{(R)}$ which makes academic information into knowledge, and $OntoReasoner^{(R)}$ which performs inference and search on the knowledge. Unlike existing search engines which provides simple search services, our system provides, based on semantic web technology, several semantic and analytic services such as year-based topic trends in academic information, related topics, topic-based researchers and institutes, researcher network, statistics and regional distribution of academic information.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.111-115
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• 2009

현재 Open API는 프로그램 개발 및 정보의 제공 등 다양한 방향에서 많이 활용 되고 있다. Open API는 개발된 기술을 웹으로 공개하여 네트워크가 연결된 곳이라면 어디서든지 그 기술을 활용할 수 있다. 잘 개발된 Open API는 매쉬업을 통해 또 다른 기술과 합쳐 새로운 서비스를 개발하는 등 그 활용빈도가 높다. 이미 개발된 기술을 활용할 수 있어서 개발 속도가 단축되고 새로운 서비스를 개발하면서 기존의 부족한 부분을 합쳐 더욱 창의적인 정보 제공이 가능하다. 이러한 Open API의 특성을 이용하여 시맨틱 기술 개발을 지원할 수 있으며, 개발 시간 단축 및 관계 설정, 온톨로지 제공 등을 네트워크를 통해 사용자들에게 제공하고 활용할 수 있는 방법을 제시한다. 시맨틱 기술은 사람이 글을 읽고 의미를 이해하는 것처럼 컴퓨터가 이해할 수 있는 언어와 규칙들을 정해 놓아 컴퓨터 사이의 의사소통을 가능하게 하는 지능형 기술이다. 이런 시맨틱 기술 개발을 지원하기 위하여 Open API를 통해 온톨로지의 개념 및 속성, 관계 설정, 코퍼스 등을 제공하기 하기 위해 CoreOnto API라는 것을 구축했다. CoreOnto API는 네트워크에서 사용할 수 있는 Open API로 세가지의 큰 형태의 Open API를 제공하고 있다. 첫 번째, 온톨로지에 대한 클래스, 속성, 관계를 제공하는 APIs, 두 번째, 트리플(두개의 용어가 어떤 관계인지를 표현한 형태로 구성(용어1, 관계, 용어2)) 생성 및 구축된 트리플을 검색할 수 APIs, 세 번째, 온톨로지를 생성하기 위해 필요한 자원(온톨로지 명, 개체명, 관계, 코퍼스 등)을 제공하는 APIs로 구성했다. 이상과 같이 시맨틱 기술 개발을 지원하기 위한 큰 형태의 세 가지 APIs를 구축하는 방법과 APIs의 활용, 그리고 서비스를 테스트한 결과를 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2049-2068
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• 2021

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.23-32
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• 2021

Semantic image segmentation technology in the field of computer vision is a technology that classifies an image by dividing it into pixels. This technique is also rapidly improving performance using a machine learning method, and a high possibility of utilizing information in units of pixels is drawing attention. However, this technology has been raised from the early days until recently for 'lack of detailed segmentation' problem. Since this problem was caused by increasing the size of the label map, it was expected that the label map could be improved by using the edge map of the original image with detailed edge information. Therefore, in this paper, we propose a post-processing algorithm that maintains semantic image segmentation based on learning, but modifies the resulting label map based on the edge map of the original image. After applying the algorithm to the existing method, when comparing similar applications before and after, approximately 1.74% pixels and 1.35% IoU (Intersection of Union) were applied, and when analyzing the results, the precise targeting fine segmentation function was improved.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1219-1232
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• 2004

Recently, the web sites, such as e-business sites and shopping mall sites, deal with lots of catalog image information and contents. As a result, it is required to support semantic-based image retrieval efficiently on such image data. This paper presents a semantic-based image retrieval system, which adopts XML and Fuzzy technology. To support semantic-based retrieval on product catalog images containing multiple objects, we use a multi-level metadata structure which represents the product information and semantics of image data. To enable semantic-based retrieval on such image data, we design a XML database for storing the proposed metadata and study how to apply fuzzy data. This paper proposes a system, generate the fuzzy data automatically to use the image metadata, that can support semantic-based image retrieval by utilizing the generating fuzzy data. Therefore, it will contribute in improving the retrieval correctness and the user's satisfaction on semantic-based e-catalog image retrieval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1885-1890
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• 2006

Because of the rapid development of information technology and easiness in using internet, Web becomes indispensible utility in every day life and various information has been shared in internet. However, caused by the flood of information in the Web, the skill of acquiring required information with easy becomes one of the most urgent on utilizing internet. As a solution fur the requirement, Semantic Web, which upgrades present web technology by using semantic skills, has been researched actively ud Ontology is regarded as the basis of constructing semantic web system. The aim of this research is on developing agent using ontology skills for botanic resources. Moreover, the system can be used for developing and accumulating various resources effectively.

• Annual Conference on Human and Language Technology
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• 2011.10a
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• pp.9-15
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• 2011

여러 언어로 작성되는 위키피디아의 경우 언어 간에 등록되어 있는 정보의 양과 내용이 달라 언어 간 정보를 상호 추출하고 서로 통합하는 연구에 대한 관심이 증가하고 있다. 특히, 위키피디아의 요약본으로써 의미가 있는 인포박스는 위키피디아 아티클에 존재하는 구조화된 정보 중 가장 근간이 되는 정보로, 본 논문에서는 위키피디아에 존재하는 인포박스를 1)소스 언어 자원으로부터 획득하여 타겟 언어로 번역하고, 2)번역된 결과물과 웹그래프를 이용하여 타겟 언어 데이터에서 획득하는 정보와 결합하는 과정을 통해 자동으로 인포박스를 생성하는 기법에 대하여 설명한다. 웹그래프는 위키피디아에 존재하는 링크 구조를 통해 서로 다른 두 용어간의 관련도를 측정하여 인포박스에 추가될 내용을 파악하는데 사용한다. 본 논문의 기법은 언어 간 인포박스를 생성하는 측면에서, 영어 인포박스 데이터를 입력으로 하여 한국어 인포박스 데이터를 생성하는 방식으로 진행하였다. 평가를 위하여 기존 한국어에 실제 존재하는 인포박스 데이터와 비교 실험하는 방식을 사용하여 평균적으로 40%의 정확률과 83%의 재현율을 나타내었다. 하지만, 기존 한국어에 존재하는 인포박스 데이터의 내용이 인포박스에 포함될 완전한 데이터를 모두 포함했다고 볼 수 없으므로 본 논문에서 제안하는 수행한 실험의 정확률이 상대적으로 낮게 나온 것으로 분석되었다. 실제 사람이 수작업으로 새롭게 생성된 인포박스 데이터의 적합성을 판별한 경우 평균 76%의 정확률과 91%의 재현율을 나타내었다.

• Journal of Information Management
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• v.35 no.3
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• pp.109-123
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• 2004

The problem of semantic interoperability in science and technology information is frequently raised. Well-established classification scheme will be used as a tool to interchange information between different databases without semantic inconsistency. However, there is still a practical barrier due to different classification schemes each database adopts. Accordingly, it is urgent to harmonize or reconcile those classifications with each other. This paper aims to solve semantic inconsistencies occurred when interchanging information between databases having different classification schemes, the Standard National Sci-Tech Classification and the Standard KISTI Classification. For the purpose a conceptual analysis of science and technology are performed and five consistency/inconsistency types are analyzed based on some examples.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.71-80
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• 2002

Automatic language translation has greatly advanced due to the increased user needs and Information retrieval in WWW. This paper utilizes the integrated knowledge bases of noun and verb networks for automatic translation of ambiguous words in the Korean sentences, through the selectional restriction relation in the sentences. And this paper presents the method to verify validity of Korean noun semantic networks that are used for the construction of the selectional restriction relation by applying the networks to the syntactic and semantic properties Integration of Korean Noun Networks into the SENKOV system will provide the accurate and efficient knowledge bases for the semantic analysis of Korean NLP.