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Finding topic-level experts in scholarly networks.

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Title: Finding topic-level experts in scholarly networks.
Authors: Lin, Lili1 linlili@hhu.edu.cn, Xu, Zhuoming1, Ding, Ying2, Liu, Xiaozhong3
Source: Scientometrics. Dec2013, Vol. 97 Issue 3, p797-819. 23p.
Subjects: Expert computer system software, Cooperative research, Personal information management, Systems design, Information retrieval, Comparative studies, Algorithms
Abstract: Expert finding is of vital importance for exploring scientific collaborations to increase productivity by sharing and transferring knowledge within and across different research areas. Expert finding methods, including content-based methods, link structure-based methods, and a combination of content-based and link structure-based methods, have been studied in recent years. However, most state-of-the-art expert finding approaches have usually studied candidates’ personal information (e.g. topic relevance and citation counts) and network information (e.g. citation relationship) separately, causing some potential experts to be ignored. In this paper, we propose a topical and weighted factor graph model that simultaneously combines all the possible information in a unified way. In addition, we also design the Loopy Max-Product algorithm and related message-passing schedules to perform approximate inference on our cycle-containing factor graph model. Information Retrieval is chosen as the test field to identify representative authors for different topics within this area. Finally, we compare our approach with three baseline methods in terms of topic sensitivity, coverage rate of SIGIR PC (e.g. Program Committees or Program Chairs) members, and Normalized Discounted Cumulated Gain scores for different rankings on each topic. The experimental results demonstrate that our factor graph-based model can definitely enhance the expert-finding performance. [ABSTRACT FROM AUTHOR]
Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Finding topic-level experts in scholarly networks.
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  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Lili%22">Lin, Lili</searchLink><relatesTo>1</relatesTo><i> linlili@hhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhuoming%22">Xu, Zhuoming</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ding%2C+Ying%22">Ding, Ying</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaozhong%22">Liu, Xiaozhong</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Scientometrics%22">Scientometrics</searchLink>. Dec2013, Vol. 97 Issue 3, p797-819. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Expert+computer+system+software%22">Expert computer system software</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+research%22">Cooperative research</searchLink><br /><searchLink fieldCode="DE" term="%22Personal+information+management%22">Personal information management</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+design%22">Systems design</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
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  Data: Expert finding is of vital importance for exploring scientific collaborations to increase productivity by sharing and transferring knowledge within and across different research areas. Expert finding methods, including content-based methods, link structure-based methods, and a combination of content-based and link structure-based methods, have been studied in recent years. However, most state-of-the-art expert finding approaches have usually studied candidates’ personal information (e.g. topic relevance and citation counts) and network information (e.g. citation relationship) separately, causing some potential experts to be ignored. In this paper, we propose a topical and weighted factor graph model that simultaneously combines all the possible information in a unified way. In addition, we also design the Loopy Max-Product algorithm and related message-passing schedules to perform approximate inference on our cycle-containing factor graph model. Information Retrieval is chosen as the test field to identify representative authors for different topics within this area. Finally, we compare our approach with three baseline methods in terms of topic sensitivity, coverage rate of SIGIR PC (e.g. Program Committees or Program Chairs) members, and Normalized Discounted Cumulated Gain scores for different rankings on each topic. The experimental results demonstrate that our factor graph-based model can definitely enhance the expert-finding performance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11192-013-0988-6
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              Text: Dec2013
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