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    袁莎, 唐杰, 顾晓韬. 开放互联网中的学者画像技术综述[J]. 计算机研究与发展, 2018, 55(9): 1903-1919. DOI: 10.7544/issn1000-1239.2018.20180139
    引用本文: 袁莎, 唐杰, 顾晓韬. 开放互联网中的学者画像技术综述[J]. 计算机研究与发展, 2018, 55(9): 1903-1919. DOI: 10.7544/issn1000-1239.2018.20180139
    Yuan Sha, Tang Jie, Gu Xiaotao. A Survey on Scholar Profiling Techniques in the Open Internet[J]. Journal of Computer Research and Development, 2018, 55(9): 1903-1919. DOI: 10.7544/issn1000-1239.2018.20180139
    Citation: Yuan Sha, Tang Jie, Gu Xiaotao. A Survey on Scholar Profiling Techniques in the Open Internet[J]. Journal of Computer Research and Development, 2018, 55(9): 1903-1919. DOI: 10.7544/issn1000-1239.2018.20180139

    开放互联网中的学者画像技术综述

    A Survey on Scholar Profiling Techniques in the Open Internet

    • 摘要: 开放互联网中的学者画像工作是近年来的研究热点问题.学者画像的目标是提取学者各维度的属性信息进行信息挖掘和分析应用.学者画像技术是大型智库实现专家发现、学术影响力评估等功能的关键.在开放互联网中,学者画像面临数据量大、数据噪音和数据冗余等新挑战.这使得传统的用户画像理论、模型和方法无法直接无缝地移植到开放互联网环境下的用户画像系统中.针对这些挑战,对现有学者画像技术进行了总结和分类,为进一步的研究工作提供参考.首先分析了学者画像问题,对学者画像的基础理论——信息抽取方法——进行了总体概述,详细总结了各种可用模型与方法;对实现学者画像的基本任务包括学者信息标注、研究兴趣挖掘和学术影响力预测进行了详细阐述;介绍了学者画像应用实例AMiner系统;对未来重点的研究内容和发展方向进行了探讨和展望.

       

      Abstract: Scholar profiling from the open Internet has become a hot research topic in recent years. Its goal is to extract the attribute information of a scholar. Scholar profiling is a fundamental issue in large-scale expert databases for finding experts, evaluating academic influence, and so on. In the open Internet, scholar profiling faces new challenges, such as large amount of data, data noise and data redundancy. The traditional user profiling methods and algorithms cannot be directly used in the user profiling system in the open Internet environment. In this paper, the existing technologies are summarized and classified to provide reference for further research. Firstly, we analyze the problem of scholar profiling, and give a general overview of the information extraction method, which is the basic theory of user profiling. Then, the three basic tasks of scholar profiling including scholar information annotation, research interest mining and academic impact prediction are introduced in detail. What’s more, the successful application system of scholar profiling called AMiner is introduced. Finally, open research issues are discussed and possible future research directions are prospected.

       

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