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Bibliometric-enhanced retrieval models for big scholarly information systems

机译:大型学术信息系统的文献计量增强检索模型

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摘要

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this paper we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, can improve retrieval services for specific communities, as well as for large, cross-domain large collections. This paper aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics / scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface.
机译:文献计量技术虽然可以为用户提供增值效果,但尚未广泛用于增强数字图书馆的检索过程。在本文中,我们将探讨奖学金的统计建模(例如Bradfordizing或共同作者网络的网络分析)如何改善特定社区以及大型,跨域大型馆藏的检索服务。本文旨在提高人们对信息检索(IR)与文献计量学/科学计量学之间缺少联系的认识,并为在数字图书馆界面将文献计量学增强的服务纳入检索提供一个共识。

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