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数据分析与知识发现  2017, Vol. 1 Issue (2): 1-10     https://doi.org/10.11925/infotech.2096-3467.2017.02.01
  综述评介 本期目录 | 过刊浏览 | 高级检索 |
专家检索与专家排名研究评述*
叶光辉(), 夏立新
华中师范大学信息管理学院 武汉 430079
Review of Expert Retrieval and Expert Ranking Studies
Ye Guanghui(), Xia Lixin
School of Information Management, Central China Normal University, Wuhan 430079, China
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摘要 

目的】对已有专家检索与专家排名方法进行评述, 为后续研究提供理论基础。【文献范围】从Web of Science (WOS)、CNKI等数据库中分别以“专家检索(Expert Retrieval)”、“专家排名(Expert Ranking)”、“排名融合(Ranking Fusion)”等为检索词搜集获得相关文献65篇。【方法】针对专家检索覆盖面不足及专家特征计算量大两方面问题, 从专家检索评测和排名融合两个角度梳理并评析现有的研究进展。【结果】融合关系属性是目前专家检索方法的主流, 检索结果可信度研究主要依据用户满意度和文档可信度开展; 专家排名采用友邻推荐模型、PageRank、D-S理论、社交网络与复杂网络分析等实现排名及排名融合, 融合结果总体优于基准排名。【局限】不同排名融合方法间的横向对比研究较少。【结论】相关研究可为构建信息融合视角下的专家会诊平台提供参考, 具体体现在专家信息组织、专家遴选和专家意见融合环节。

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叶光辉
夏立新
关键词 专家检索排名融合社交网络关系属性效果评测    
Abstract

[Objective] This paper reviews the expert retrieval and expert ranking literature to provide theoretical foundations for future studies. [Coverage] 65 papers were retrieved from the Web of Science (WOS), CNKI and other databases using the keywords of “expert retrieval”, “expert ranking”, and “ranking fusion”. [Methods] We analyzed research evaluating expert retrieval and fusion rankings, aiming to solve the issues of insufficiency of expert coverage and heavy computation of expert features. [Results] We found that most expert retrieval system adopted the relationship attribute fusion method, and the credibility of search results was decided by the users’ satisfaction and quality of the retrieved documents. Expert ranking was established by FRM, PageRank, D-S theory, social network and complex network analysis. Empirical research showed that the fusion ranking results were generally better than the baseline ones. [Limitations] More comparison of research among different ranking methods was needed. [Conclusions] Related studies help us building expert consulting platform from the perspective of expert information organization, expert selection and expert opinion fusion.

Key wordsExpert Retrieval    Ranking Fusion    Social Network    Relationship Attribute    Effect Evaluation
收稿日期: 2016-09-12      出版日期: 2017-03-27
ZTFLH:  G350  
基金资助:*本文系国家社会科学基金重大项目“基于多维度聚合的网络资源知识发现研究”(项目编号: 13&ZD183)、中央高校基本科研业务费项目“面向应急决策的专家发现与意见融合研究”(项目编号: CCNU16A05044)和国家自然科学基金青年项目“多因素融合下的微博话题可信度评估模型及实证研究”(项目编号: 71303179)的研究成果之一
引用本文:   
叶光辉, 夏立新. 专家检索与专家排名研究评述*[J]. 数据分析与知识发现, 2017, 1(2): 1-10.
Ye Guanghui,Xia Lixin. Review of Expert Retrieval and Expert Ranking Studies. Data Analysis and Knowledge Discovery, 2017, 1(2): 1-10.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.02.01      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I2/1
  专家检索与专家排名的两种模式
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