The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1663829
- Type
- E - Conference contribution
- DOI
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10.1007/978-3-030-30760-8_26
- Title of conference / published proceedings
- Lecture Notes in Computer Science
- First page
- 296
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2019
- URL
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http://www.tpdl.eu/tpdl2019/
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, shortlisted for the Best Paper Award at TPDL 2019, introduces an ontology-driven approach to classifying scholarly publications, which outperforms alternative methods. The approach has been adopted in both industry (Springer Nature) and academia (e.g., Vergoulis et al., 2020, Dörpinghaus et al., 2020). Springer Nature (Editorial Director, details on request) uses for classifying about 800 computer science proceedings yearly. Vergoulis et al. utilized it to augment a Scholarly Knowledge Graph with topics, to support expert search. Dörpinghaus et al. adopted it in context of generating a knowledge graph from the biomedical literature, to support question answering and knowledge discovery.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -