A survey on expert finding techniques

Lin Shuyi; Hong Wenxing; Wang Dingding; Li Tao

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A survey on expert finding techniques

机译：专家发现技术调查

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摘要

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.

机译：在特定领域中寻找专家是一项重要任务，并且在信息检索社区中引起了很多关注。在过去的几十年中，对该主题的研究取得了重大进展，并提出了各种技术。在本次调查中，我们回顾了专家发现中的最新方法，并根据其基本算法和模型将这些方法归纳为不同的类别。我们还将介绍用于评估专家发现系统的最广泛使用的数据收集，并讨论未来的研究方向。

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来源
《Journal of Intelligent Information Systems》 |2017年第2期|255-279|共25页
作者
Lin Shuyi; Hong Wenxing; Wang Dingding; Li Tao;
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作者单位

Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China|Shanghai Jiao Tong Univ, Key Lab Syst Control & Informat Proc, Minist Educ, Shanghai, Peoples R China;

Xiamen Univ, Dept Automat, Xiamen, Peoples R China;

Florida Atlantic Univ, Dept CEECS, Boca Raton, FL 33431 USA;

Florida Int Univ, Sch Comp Sci, Miami, FL 33199 USA|Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China;

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原文格式 PDF
正文语种 eng
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关键词
Expert finding; Data resources; Algorithms;

机译：专家发现;数据资源;算法;

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机译：我们开发了一种科学创造力模型，并在稀有疾病领域进行测试。我们的模型基于对Rett综合征的深入案例研究的结果。档案分析，文献计量技术和专家调查与网络分析相结合，以确定最具创造力的科学家。首先，比较生成和组合创造力的替代措施。然后，我们推广了我们的结果，并提出了社会语义网络演化的随机模型。模型预测用多个罕见疾病专业网络进行测试。我们发现，一个领域的专家之间的新的科学合作增强了组合创造力。相反，新手的高入门率与生成创造力负相关。通过扩展这组有用的概念，创造性的科学家获得了中心地位。同时，通过增加科学界的中心地位，科学家们可以复制和推广他们的结果，从而促进科学范式的发展。

A survey on expert finding techniques

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