计算机科学
出版
分类器(UML)
数据科学
领域(数学)
图形
情报检索
科学领域
科学文献
多学科方法
万维网
人工智能
工程类
纯数学
法学
古生物学
社会学
生物
理论计算机科学
机械工程
社会科学
数学
工作(物理)
政治学
作者
Nikolaos Gialitsis,Sotiris Kotitsas,Haris Papageorgiou
出处
期刊:Cornell University - arXiv
日期:2022-04-02
标识
DOI:10.1145/3487553.3524677
摘要
Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organised in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a publications' dataset extracted from Microsoft Academic Graph and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.
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