• 한국언어정보학회지:언어와정보
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• 제14권1호
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• pp.165-196
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• 2010

The primary goal of this paper is to propose methods of enriching two heterogeneous ontologies: Sejong Semantic Classes (SJSC) and KorLexNoun 1.5 (KLN). In order to achieve this goal, this study introduces the pros and cons of two ontologies, and analyzes the error patterns found during the fine-grained manual mapping processes between them. Error patterns can be classified into four types: (1) structural defectives involved in node branching, (2) errors in assigning the semantic classes, (3) deficiency in providing linguistic information, and (4) lack of the lexical units representing specific concepts. According to these error patterns, we propose different solutions in order to correct the node branching defectives and the semantic class assignment, to complement the deficiency of linguistic information, and to increase the number of lexical units suitably allotted to their corresponding concepts. Using the results of this study, we can obtain more enriched ontologies by correcting the defects and errors in each ontology, which will lead to the enhancement of practicality for syntactic and semantic analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• 한국인터넷방송통신학회논문지
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• 제17권3호
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• pp.121-127
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• 2017

최근 공간 데이터와 같은 2차원 데이터를 위한 Semantic Web에 대한 연구가 활발하게 진행되고 있다. 2차원 Semantic Web은 기존의 Geospatial Web과 Semantic Web이 접목되어 다양한 지리 공간 정보와 일반 웹 상의 방대한 비공간 정보를 효율적으로 연계 및 통합하여 제공할 수 있는 지능적인 지리정보 웹 서비스 기술이다. 하지만 다차원 데이터 처리를 위한 연구는 전체적으로 부족한 편이며, 관련 표준 역시 제정되어 있지 않다. 따라서 본 논문에서는 그동안 진행되었던 Ontology 처리 기술과 관련된 다양한 기반 이론 및 기술들을 적용하여 다차원 데이터 처리가 가능한 온톨로지, 질의, 추론에 대한 내용을 제안하였다. 또한 각각 제안한 내용을 다차원 질의가 필요한 가상 시나리오에 적용해보았다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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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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• 제9권5호
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• pp.1807-1824
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• 2015

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (B)
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• pp.238-240
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• 2002

If there is an Information Retrieval system which comprehends the semantic content of documents and knows the preference of users. the system can search the information better on the Internet, or improve the IR performance. Therefore we propose the IR model which combines semantic based indexing and fuzzy relevance model. In addition to the statistical approach, we chose the semantic approach in indexing, lexical chains, because we assume it would improve the performance of the index term extraction. Furthermore, we combined the semantic based indexing with the fuzzy model, which finds out the exact relevance of the user preference and index terms. The proposed system works as follows: First, the presented system indexes documents by the efficient index term extraction method using lexical chains. And then, if a user tends to retrieve the information from the indexed document collection, the extended IR model calculates and ranks the relevance of user query. user preference and index terms by some metrics. When we experimented each module, semantic based indexing and extended fuzzy model. it gave noticeable results. The combination of these modules is expected to improve the information retrieval performance.

• 한국IT서비스학회지
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• 제7권3호
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• pp.253-269
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• 2008

In this paper, we present an application called Semantic Search which is built on different supporting technologies and is designed to improve traditional web searching. The Semantic Search is becoming crucial challenges on semantic web. The assessment and the implementation of the research on Semantic Search is not full-fledged whereas its research is highly interested. Also there exists only little research that offers a commercial use Semantic Search System that should be taken into the account in measuring the effectiveness of a Semantic Search System. This paper proposes an implementation and evaluation for the Semantic Search System. Firstly, we built Semantic Search System which includes a case of development and it's procedure. Secondly, We presented the measurement of our Semantic Search System's effectiveness. Finally, the evaluation offers useful implications to the researchers and practitioners to improve the research level to the commercial use.

• Asia pacific journal of information systems
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• 제19권1호
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• pp.33-62
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• 2009

Internet computing is a disruptive IT innovation. Semantic Web can be considered as an IT innovation because the Semantic Web technology possesses the potential to reduce information overload and enable semantic integration, using capabilities such as semantics and machine-processability. How should organizations adopt the Semantic Web? What factors affect the adoption and diffusion of Semantic Web innovation? Most studies on adoption and diffusion of innovation use empirical analysis as a quantitative research methodology in the post-implementation stage. There is criticism that the positivist requiring theoretical rigor can sacrifice relevance to practice. Rapid advances in technology require studies relevant to practice. In particular, it is realistically impossible to conduct quantitative approach for factors affecting adoption of the Semantic Web because the Semantic Web is in its infancy. However, in an early stage of introduction of the Semantic Web, it is necessary to give a model and some guidelines and for adoption and diffusion of the technology innovation to practitioners and researchers. Thus, the purpose of this study is to present a model of adoption and diffusion of the Semantic Web and to offer propositions as guidelines for successful adoption through a qualitative research method including multiple case studies and in-depth interviews. The researcher conducted interviews with 15 people based on face-to face and 2 interviews by telephone and e-mail to collect data to saturate the categories. Nine interviews including 2 telephone interviews were from nine user organizations adopting the technology innovation and the others were from three supply organizations. Semi-structured interviews were used to collect data. The interviews were recorded on digital voice recorder memory and subsequently transcribed verbatim. 196 pages of transcripts were obtained from about 12 hours interviews. Triangulation of evidence was achieved by examining each organization website and various documents, such as brochures and white papers. The researcher read the transcripts several times and underlined core words, phrases, or sentences. Then, data analysis used the procedure of open coding, in which the researcher forms initial categories of information about the phenomenon being studied by segmenting information. QSR NVivo version 8.0 was used to categorize sentences including similar concepts. 47 categories derived from interview data were grouped into 21 categories from which six factors were named. Five factors affecting adoption of the Semantic Web were identified. The first factor is demand pull including requirements for improving search and integration services of the existing systems and for creating new services. Second, environmental conduciveness, reference models, uncertainty, technology maturity, potential business value, government sponsorship programs, promising prospects for technology demand, complexity and trialability affect the adoption of the Semantic Web from the perspective of technology push. Third, absorptive capacity is an important role of the adoption. Fourth, suppler's competence includes communication with and training for users, and absorptive capacity of supply organization. Fifth, over-expectance which results in the gap between user's expectation level and perceived benefits has a negative impact on the adoption of the Semantic Web. Finally, the factor including critical mass of ontology, budget. visible effects is identified as a determinant affecting routinization and infusion. The researcher suggested a model of adoption and diffusion of the Semantic Web, representing relationships between six factors and adoption/diffusion as dependent variables. Six propositions are derived from the adoption/diffusion model to offer some guidelines to practitioners and a research model to further studies. Proposition 1 : Demand pull has an influence on the adoption of the Semantic Web. Proposition 1-1 : The stronger the degree of requirements for improving existing services, the more successfully the Semantic Web is adopted. Proposition 1-2 : The stronger the degree of requirements for new services, the more successfully the Semantic Web is adopted. Proposition 2 : Technology push has an influence on the adoption of the Semantic Web. Proposition 2-1 : From the perceptive of user organizations, the technology push forces such as environmental conduciveness, reference models, potential business value, and government sponsorship programs have a positive impact on the adoption of the Semantic Web while uncertainty and lower technology maturity have a negative impact on its adoption. Proposition 2-2 : From the perceptive of suppliers, the technology push forces such as environmental conduciveness, reference models, potential business value, government sponsorship programs, and promising prospects for technology demand have a positive impact on the adoption of the Semantic Web while uncertainty, lower technology maturity, complexity and lower trialability have a negative impact on its adoption. Proposition 3 : The absorptive capacities such as organizational formal support systems, officer's or manager's competency analyzing technology characteristics, their passion or willingness, and top management support are positively associated with successful adoption of the Semantic Web innovation from the perceptive of user organizations. Proposition 4 : Supplier's competence has a positive impact on the absorptive capacities of user organizations and technology push forces. Proposition 5 : The greater the gap of expectation between users and suppliers, the later the Semantic Web is adopted. Proposition 6 : The post-adoption activities such as budget allocation, reaching critical mass, and sharing ontology to offer sustainable services are positively associated with successful routinization and infusion of the Semantic Web innovation from the perceptive of user organizations.

• 한국비블리아학회지
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• 제23권1호
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• pp.91-111
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• 2012

웹 정보의 폭발적인 성장과 함께, 단순히 한 두 개의 키워드의 입력에 따른 검색은 너무 많은 검색결과를 가져오게 되기 때문에 전통적인 정보검색기법은 이용자들에게 있어서 만족할 수 없는 결과를 제공하고 있다. 본 연구에서는 정보에 대한 의미를 기반으로 정보검색의 질적 향상을 위한 기술의 개발을 목표로 하고 있다. 이를 위하여 시멘틱 웹 기술에서 요구되는 시멘틱 기반 검색에 대한 최근의 연구동향 및 기술을 분석하여 시멘틱 기반 검색시스템에서 요구사항을 파악하고, 지능형 검색시스템의 아키텍처, 시멘틱 검색 서비스 개발 과정과 핵심기술 등을 살펴보았다. 분석결과와 함께, 시멘틱 기반 정보검색 시스템의 전체적인 아키텍처에 대한 설계 및 요구사항을 제안하였다.

• 인지과학
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• 제12권1_2호
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• pp.11-24
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• 2001

명사열의 의미해석은 명사들 사이의 의미적인 관계를 찾는 것으로, 한국어에서 명사열의 출현은 보편적인 현상이며, 그 생성 또한 비교적 자유롭다. 본 논문에서는 기계가독형사전과 코퍼스로부터 명사 사이의 <목적>, <물건-재료>, <원인>등과 같은 의미관계 정보를 자동으로 추출한다. 추출한 의미관계정보에 기반하여 의미망을 구축하고. 의미 정보와 서술성 명사의 하위 범주 정보를 이용하여 명사열을 해석하는 방법을 제안하였다. 본 논문에서는 명사열의의미 해석 대상을 한국어 명사열의 대부분의 차지하는 수싲 명사+핵심명사 형태로 한정하였다. 기계가독형사전과 코퍼스로부터 추출한 의미정보와 하위 범주를 이용한 명사열의미 해석은 기존의 기계가독형사전 기반 의미 해석보다 정확률 +40.30%,적용률+12.73%의 성능 향상을 나나태었다.