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Developing an Ontology for Personal Debt and Its Application to Social Big Data
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소셜 빅데이터 분석을 위한 가계부채 온톨로지 개발

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Type
Academic journal
Author
Journal
연세대학교 사회복지연구소 한국사회복지조사연구 한국사회복지조사연구 제63권 KCI Accredited Journals
Published
2019.1
Pages
5 - 34 (30page)

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Developing an Ontology for Personal Debt and Its Application to Social Big Data
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Research history (2)

2019

소셜 빅데이터 분석을 위한 가계부채 온톨로지 개발
구서정 ,  박정민 ,  송태민 and others 1 한국사회복지학회 학술대회 자료집 2019.10 Proceeding
Developing an Ontology for Personal Debt and Its Application to Social Big Data
Gu， Sea juang ,  Pa ,  k， Jung Min and others 2 한국사회복지조사연구 2019.01 Academic journal
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This study is about developing an ontology for personal debt and its application to social big data. For developing an ontology, we identified major concepts/classes in the area of personal debt, arranged the concepts in a hierarchy, and described properties of the concept (Noy and McGuinness, 2001). This study is based on 1) the review of the literature related to personal debt and 2) the exploration of social big data, that in this study refers to approximately 4 million debt-related online documents for 5 years from 2014 to 2018 collected through a variety of publicly available online sources. The concepts related to personal debt were categorized as 4 classes-sociodemographic characteristics, associated factors, properties of debt, and consequences of debt. Notable properties of each class with a high frequency in online documents were as follows: Sociodemographic characteristics include employees, self-employees, and families; Associated factors include interest rate, housing, and fraud; Properties of debt include subprime loans, prime loans, mobile loans, and mortgage; Consequences of debt include repayment, personal bankruptcy, and arrear. Sentiments frequently shown include both positive ones (e.g., practicality, efforts) and negative ones (e.g., hopelessness, agony, worry). This study is, to our knowledge, the first that presents an ontology of personal debt, and it provides a useful guideline for analysis of big data that future research in this area can build on.
#social big data #ontology #personal debt #household debt #over-indebtedness

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