摘要
【目的/意义】网络学术资源的爆炸式增长,使信息遴选变得越来越困难。高效、快速地遴选出高质量的学术资源,有助于降低学者的信息搜寻成本、提高工作效率。【方法/过程】本文融合知识图谱和用户画像两种当前在信息遴选中具有优异表现的技术,构建了一个网络学术资源遴选的框架模型。【结果/结论】实验发现,融合了知识图谱和学者画像的网络学术资源遴选方法,通过网络学术资源间的语义关系网与学者需求、偏好的结合,能够有效提升学术资源遴选的质量和效率。【创新/局限】本文构建的学术资源遴选框架既克服了知识图谱不全的问题,又兼顾了学者个体和群体需求,有效提升学术资源遴选的效率、质量及精准度。然而本研究在知识图谱的构建中还没有考虑时间维度,用户画像也有待进一步深入。
【Purpose/significance】The explosive growth of online academic resources makes information selection more and more difficult.Efficient and rapid selection of high-quality academic resources help to reduce the cost of information searching and improve the work efficiency of scholars.【Method/process】In this paper,a framework model of online academic resource selection is constructed by combining knowledge graph and user profiling.【Result/conclusion】The experiment shows that the framework can effectively improve the quality and efficiency of academic resources selection by combining the semantic network of online academic resources with the needs and preferences of scholars.【Innovation/limitation】This framework not only overcome the incomplete problem of knowledge graph,but also take into account the needs of individual and group scholar,and effectively improve the efficiency,quality and accuracy of academic resource selection.However,this study has not considered the time dimension in the knowledge graph,and the user profiling needs to be further explored.
作者
何迎朝
李慧佳
HE Yingzhao;LI Huijia(Fudan Development Institute,Shanghai 200433,China;School of Management,Northwest Normal University,Lanzhou 730070,China;Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China)
出处
《情报科学》
CSSCI
北大核心
2024年第9期72-81,111,共11页
Information Science
基金
国家社科基金青年项目“基于语义关联的专题学术资源遴选框架研究”(19CTQ007)
关键词
知识图谱
学者画像
网络学术资源遴选
遴选框架
融合
knowledge graph
scholar profiling
network academic resources selection
selection framework
combining
