A Unified Approach for Learning Expertise and Authority in Digital Libraries
A Unified Approach for Learning Expertise and Authority in Digital Libraries
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DOI：
10.1007/978-3-319-55699-4_22
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Baptiste de La Robertie;Liana Ermakova;Y. Pitarch;A. Takasu;O. Teste
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作者：
Baptiste de La Robertie;Liana Ermakova;Y. Pitarch;A. Takasu;O. Teste
在任何行业组织中，管理个人专业知识都是一个主要问题。如果以前的工作已经广泛地研究了相关的专业知识和权威剖析问题，他们假设这两个关键概念是语义独立的。在数字图书馆中，最先进的模型通常只使用文本信息来总结研究人员的个人资料。因此，拥有大量出版物的作者被机械地培养出来，而不是那些产量较低、可能具有更高专业知识的作者。为了克服这一缺陷，我们建议将专业知识和权威的两种表示形式合并，并通过捕捉这两种概念之间的相互强化原则来平衡结果。基于图书馆的图形表示，专家侧写任务被描述为一个优化问题，其中潜在的专业知识和权威表示被同时学习，从而使拥有大量出版物的个人的专业知识得分不偏。这项建议是在一个公共科学书目数据集上例证的，在这个数据集上，研究人员的出版物被视为个人专业知识的证据来源，以及作为权威信号来源的引用关系。我们在微软学术搜索数据库上进行的实验结果表明，与最先进的专家检索任务模型相比，效率有了显著提高。
Managing individual expertise is a major concern within any industrial-wide organization. If previous works have extensively studied the related expertise and authority profiling issues, they assume a semantic independence of these two key concepts. In digital libraries, state-of-the-art models generally summarize the researchers’ profile by using solely textual information. Consequently, authors with a large amount of publications are mechanically fostered to the detriment of less prolific ones with probably higher expertise. To overcome this drawback we propose to merge the two representations of expertise and authority and balance the results by capturing a mutual reinforcement principle between these two notions. Based on a graph representation of the library, the expert profiling task is formulated as an optimization problem where latent expertise and authority representations are learned simultaneously, unbiasing the expertise scores of individuals with a large amount of publications. The proposal is instanciated on a public scientific bibliographic dataset where researchers’ publications are considered as a source of evidence of individuals’ expertise and citation relations as a source of authoritative signals. Results from our experiments conducted over the Microsoft Academic Search database demonstrate significant efficiency improvement in comparison with state-of-the-art models for the expert retrieval task.