• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.1119-1121
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• 2014

저널 논문 투고 및 심사시스템에서의 논문 제출은 상시 이루어진다는 특성 때문에 논문이 제출된 시점에 적절한 심사자들을 찾아 배정하기란 쉽지 않은 문제이다. 본 논문에서는 이러한 문제를 해결하기 위하여 제출된 논문에 적절한 심사자들을 추천해주는 알고리즘을 제시하고자 한다. 심사자 추천 알고리즘에서는 해당 논문의 전문가를 심사자로써 추천하기 위하여 제출된 논문들의 키워드(Keyword)와 심사자들의 전문지식태그(Expertise Tag) 정보를 활용한다. 또한 심사자들의 기존의 심사 정보를 토대로 심사활동지수를 평가하여 이를 심사자 추천에 활용하고자 한다. 제안하는 알고리즘을 검증하기 위하여 본 논문에서는 실제 저널 논문투고시스템에 추천 알고리즘을 적용해보고 이의 결과를 제시한다.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.43-65
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• 2007

Automatic recommendation system of panel pool should be designed to support universal, expertness, fairness, and reasonableness in the process of review of proposals. In this research, we apply the theory of probabilistic ontology to measure relatedness between terms in the classification of academic domain, enlarge the number of review candidates, and rank recommendable reviewers according to their expertness. In addition, we construct a researcher network connecting among researchers according to their various relationships like mentor, coauthor, and cooperative research. We use the researcher network to exclude inappropriate reviewers and support fairness of reviewer recommendation process. Our methodology recommending proper reviewers is verified from experts in the field of proposal examination. It propose the proper method for developing a resonable reviewer recommendation system.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.431-441
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• 2008

The academic information analysis service is including automatic recommendation of reviewers and analysis of researchers' accomplishments. The service of recommendation of reviewers should be processed in a transparent, fair and accountable way. When selecting reviewers, the following information must be considered: subject of project, reviewer's maj or, expertness of reviewer, relationship between applicant and reviewer. The analysis service of researchers' accomplishments is providing statistic information of researcher, institution and location based on accomplishments including book, article, patent, report and work of art. In order to support these services, we designed ontology for academic information, converted legacy data to RDF triples, expanded knowledge appropriate to services using OntoFrame. OntoFrame is service framework which includes ontology, reasoning engine, triple store. In our study, we propose the design methodology of ontology and service system for academic information based on OntoFrame. And then we explain the components of service system, processing steps of automatic recommendation of reviewers and analysis of researchers' accomplishments.

• Annual Conference on Human and Language Technology
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• 2007.10a
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• pp.76-83
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• 2007

학술정보 분석 서비스는 학술정보 온톨로지를 사용하여 연구과제의 심사자 선정과 연구자의 연구성과 분석에 필요한 정보를 제공해 주는 서비스이다. 연구과제의 심사자 추천 서비스에서는 피심사자와 심사자의 관계, 평가자의 전문도 및 전문 분야가 사용되며, 연구성과 분석 서비스에서는 분야별/기관별 연구성과물 현황, 분야별 전문가 현황, 연구자 네트워크 등이 사용된다. 본 연구에서는 학술정보 분석 서비스를 제공하기 위해 학술정보를 온톨로지로 구축하였고, OntoFrame 기반의 추론 시스템을 적용하여 학술정보를 저장 및 확장한 후 심사자 추천 서비스와 연구성과 분석 서비스에 필요한 정보를 제공하였다. 이 논문에서는 학술정보 온톨로지의 구성과 OntoFrame 기반의 학술정보 시스템의 구성 및 서비스 방법을 제시하였고, 이를 통해 효과적인 학술정보 분석 서비스를 제공하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.711-713
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• 2013

Various human resource information of the world can be found according to spread of social network such as facebook and twitter. There are an amounts of researcher information on the science and technology area but it is difficult to find a suitable researcher for research or business such as research partner, because researcher information is not systematically arranged. To solver this problem, we are constructing authority data system for science and technology based on authority information of overseas academic papers. In this paper, in order to construct the authority data, we extracts author identification elements from millions of overseas academic papers, which are published from 1994 to 2012. There are more than 50 author identification elements such as author name, affiliation, paper title, publisher, year, keywords, co-author, co-author's affiliation in Korean, English, Chinese, and Japanese. We construct the element database by extracting and storing an author identification information based on the elements from overseas academic papers. Future works includes that the authority database for overseas academic papers is constructed by storing an academic activities of researchers after author clustering with these extracted elements. The authority data is used to improve the researcher information utilization and activate community to find a suitable research partner or a business examiner.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.