图 书 馆
计算机科学本体论：研究领域的大规模分类The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas |
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| 课程网址: | http://videolectures.net/iswc2018_salatino_ontology_areas/ |
| 主讲教师: | Angelo Antonio Salatino |
| 开课单位: | 开放大学知识媒体研究所（KMI） |
| 开课时间: | 2018-11-22 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO 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 publications. CSO 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 trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The 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 communities engaged with scholarly data. |
| 关 键 词: | 计算机科学分类法; 自动生成的研究领域本体; 预测研究趋势 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-20：cyh |
| 最后编审: | 2023-05-15：cyh |
| 阅读次数: | 32 |