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The Computer Science Ontology:A Comprehensive Automatically-Generated Taxonomy of Research Areas

Data Intelligence 2020年第3期2卷 379-416页

作者： Angelo A.Salatino Thiviyan Thanapalasingam Andrea Mannocci Aliaksandr Birukou Francesco Osborne Enrico Motta Knowledge Media Institute The Open UniversityMK76AAMilton KeynesUK Springer-Verlag GmbH Tiergartenstrasse 1769121 HeidelbergGermany

ontologies of research areas are important tools for characterizing,exploring,and analyzing the research *** fields of research are comprehensively described by large-scale taxonomies,e.g.,MeSH in Biology and PhySH in ***,current Computer Science taxonomies are coarse-grained and tend to evolve *** instance,the ACM classification scheme contains only about 2K research topics and the last version dates back to *** this paper,we introduce the Computer Science Ontology(CSO),a large-scale,automatically generated ontology of research areas,which includes about 14K topics and 162K semantic *** was created by applying the Klink-2 algorithm on a very large data set of 16M scientific *** presents two main advantages over the alternatives:i)it includes a very large number of topics that do not appear in other classifications,and ii)it can be updated automatically by running Klink-2 on recent corpora of *** powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions,such as classifying research publications,detecting research communities,and predicting research *** facilitate the uptake of CSO,we have also released the CSO Classifier,a tool for automatically classifying research papers,and the CSO Portal,a Web application that enables users to download,explore,and provide granular feedback on *** can use the portal to navigate and visualize sections of the ontology,rate topics and relationships,and suggest missing *** portal will support the publication of and access to regular new releases of CSO,with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.

关键词： scholarly data Ontology learning Bibliographic data scholarly ontologies Semantic Web

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