• Smart Media Journal
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• v.3 no.3
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• pp.20-25
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• 2014

This research proposes a concept of multi-aspect Social IoT platform that enables human, machine and service to communicate smoothly among them, as well as a means of an automatic process for transforming exiting domain knowledge representation to generic ontology representation used in the platform. Current research focuses on building a machine-based service interoperability using sensor ontology and device ontology. However, to the best of our knowledge, the research on building a semantic model reflecting multi-aspects among human, machine, and service seems to be very insufficient. Therefor, in the research we first build a multi-aspect ontology schema to transform the representation used in each domain as a part of IoT into ontology-based representation, and then develop an automatic process of generating multi-aspect IoT ontology from the domain knowledge based on the schema.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.390-394
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• 2009

The semantic web-based social network is highly useful in a variety of areas. In this paper we make diverse analyses of the FOAF-based social network, and propose an expert recommendation system. This system presents useful method of ontology-based social network using SparQL, RDFS inference, and visualization tools. Then we apply it to real social network in order to make various analyses of centrality, small world, scale free, etc. Moreover, our system suggests method for analysis of an expert on specific field. We expect such method to be utilized in multifarious areas - marketing, group administration, knowledge management system, and so on.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.10-23
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• 2019

Although social big data can provide a multi-faceted perspective on school bullying experiences among children and adolescents, the complexity and variety of unstructured text presents a challenge for systematic collection and analysis of the data. Development of an ontology, which identifies key terms and their intricate relationships, is crucial for extracting key concepts and effectively collecting data. The current study elaborated on the definition of an ontology, carefully described the 7 stage development process, and applied the ontology for collecting and analyzing school bullying social big data. As a result, approximately 2,400 key terms were extracted in top-, middle-, and lower-level categories, concerning domains of participants, causes, types, location, region, and intervention. The study contributes to the literature by explaining the ontology development process and proposing a novel alternative research model that uses social big data in school bullying research. Findings from this ontology study may provide a basis for social big data research. Practical implications of this study lie in not only helping to understand the experience of school bullying participants, but also in offering a macro perspective on school bullying as a social phenomenon.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.289-290
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• 2009

시맨틱 웹의 발전과 더불어 소셜 네트워크 자동 구축에 대한 연구가 활발히 진행되고 있다. 본 논문은 온톨로지를 기반의 소셜 정보 추출에 대한 방법을 다루고 있으며, 특히, 이에 필요한 온톨로지 모델링, 사람들 사이의 관계 추출을 위한 패턴 정의에 대해 기술하고 있다. 온톨로지와 패턴을 기반으로 역사적 인물들의 친족관계를 파악함으로써 소설 정보의 추출에 대한 가능성을 미리 짐작해 본다.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.485-495
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• 2009

We in this paper propose a social network based on ontology in Korea Institute of Oriental Medicine (KIOM). By using the social network, researchers can find collaborators and share research results with others. For this purpose, first, personal profiles, scholarships, careers, licenses, academic activities, research results, and personal connections for all of researchers in KIOM are collected. After relationship and hierarchy among ontology classes and attributes of classes are defined through analyzing the collected information, a social network ontology are constructed using FOAF and OWL. This ontology can be easily interconnected with other social network by FOAF and provide the reasoning based on OWL ontology.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.343-352
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• 2007

최근 Web 2.0 발달과 더불어 블로그나 온라인 카페 등 웹 상의 사용자가 개인적인 정보를 자유롭게 개제할 수 있도록 하는 인터넷서비스가 증가하면서, 이 사용자들 간의 관계에 초점을 맞춘 소셜 네트워크 분양의 연구가 활발히 이루어지고 있다. 하지만 많은 소셜 네트워크 서비스가 정보자원을 컴퓨터가 처리할 수 있는 의미적인 정보로 표현하고 있지 않기 때문에 서로 다른 도메인 간에 공유와 재사용이 어렵고, 사회적 개체들 간의 관계가 명확하게 정의되어 있지 않아 소셜 네트워크 분석에 어려움이 있다. 본 논문에서는 가상 커뮤니티 사용자들이 업로드 한 사진 데이터를 이용한 시맨틱 웹 기반의 소셜 네트워크 분석 시스템을 제안한다. 온톨로지를 기반으로 사진에서 추출된 얼굴 개체와의 관계와 이미 인맥 관계를 형성하고 있는 사람들의 정보적 연결성을 명확하게 정의하고 도메인 규칙을 활용하여 의미 있는 사회적 연결 관계를 추론한다. 이를 그래프로 시각화하여 사용자에게 제공함으로써 온라인 상에서 형성된 커뮤니티 내에서 효율적인 소셜 네트워크 분석을 도모하고 이를 기반으로 다양한 응용 분야에 활용하는 방법을 모색한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.927-928
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• 2009

본 연구는 웹2.0의 기술로 각광받는 소셜네트워크 서비스를 위해 다양한 분야에서 적용 가능 할 수 있는 시맨틱 표현 모델을 제안하는 것이다. 소셜네트워크를 온톨로지를 이용하여 다양한 영역으로 추론하고 확장하여 서비스 제공자 중심의 일방적 소셜네트워크 제공이 아닌, 사용자가 질의에 대한 의미를 분석하고 결정하여 사용자 의도에 부합하는 상호작용 가능한 소셜네트워크 서비스를 제공할 수 있는 온톨로지 기반 시맨틱 표현 모델과 이를 적용하기 위한 전체 시스템 구조를 제안하고자 한다. 본 연구를 통해 소셜네트워크 서비스가 다양한 분야로 확대 적용할 수 있을 것으로 기대된다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.157-162
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• 2008

This paper presents a learning method of tagging ontology using large tagging data such as a folksonomy, which stands for classification structure informally created by the people. There is no common agreement about the semantics of a tagging, and most social web sites internally use different methods to represent tagging information, obstructing interoperability between sites and the automated processing by software agents. To solve this problem, we need a tagging ontology, defined by analyzing intrinsic attributes of a tagging. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tagging ontology is also suggested as an applying field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.183-190
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• 2012

This study constructs an expert search system which has a mutual cooperation function based on thesis and author profile. The proposed methodology is as follows. First, we propose weighting method which can search a keyword and the most relevant keyword. Second, we propose a method which can search the experts efficiently with this weighting method. On the preferential basis, keywords and author profiles are extracted from the papers, and experts can be searched through this method. This system will be available to many fields of social network. However, this information is distributed to many systems. We propose a method using multi-ontology to integrate distributed data. The multi-ontology is composed of meta ontology, instance ontology, location ontology and association ontology. The association ontology is constructed through analysis of keyword association dynamically. An expert network is constructed using this multi-ontology, and this expert network can search expert through association trace of keyword. The expert network can check the detail area of expertise through the research list which is provided by the system.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.215-230
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• 2009

We propose semantic model that is possible to apply for the bibliographic metadata of domestic digital library by analysing bibliographic metadata models like MARC, DC, MODS, JeromeDL's metadata model MarcOnt as the representative case of semantic digital library and FRBR model as the conceptual model.