New trends in scientific knowledge graphs and research impact assessment
New trends in scientific knowledge graphs and research impact assessment
复制标题
DOI：
10.1162/qss_e_00160
复制
发表时间：
2021-11
影响因子：
6.4
通讯作者：
P. Manghi;Andrea Mannocci;Francesco Osborne;Dimitris Sacharidis;Angelo Salatino;Thanasis Vergoulis
中科院分区：
文献类型：
--
作者：
P. Manghi;Andrea Mannocci;Francesco Osborne;Dimitris Sacharidis;Angelo Salatino;Thanasis Vergoulis
近几十年来，我们经历了科学文章和相关研究对象（例如，数据集、软件包）。随着这一趋势的不断发展，学术知识领域的从业者面临着一些挑战。在这个特殊的问题，我们专注于两大类这样的挑战：（a）那些与学术数据的组织，以实现一个灵活的，上下文敏感的，细粒度的，和机器可操作的学术知识表示，在同一时间是结构化的，相互关联的，语义丰富，和（B）那些与设计新颖的，可靠的，和全面的指标，以评估科学的影响。
In recent decades, we have experienced a continuously increasing publication rate of scientific articles and related research objects (e.g., data sets, software packages). As this trend keeps growing, practitioners in the field of scholarly knowledge are confronted with several challenges. In this special issue, we focus on two major categories of such challenges: (a) those related to the organization of scholarly data to achieve a flexible, context-sensitive, finegrained, and machine-actionable representation of scholarly knowledge that at the same time is structured, interlinked, and semantically rich, and (b) those related to the design of novel, reliable, and comprehensive metrics to assess scientific impact.