Abstract
Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
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Notes
- 1.
Medical Subject Headings: https://www.nlm.nih.gov/mesh/.
- 2.
PhySH - Physics Subject Headings: https://physh.aps.org.
- 3.
STW Thesaurus for Economics: http://zbw.eu/stw.
- 4.
Scopus - https://www.scopus.com.
- 5.
Dimensions - https://www.dimensions.ai.
- 6.
Semantic Scholar - https://www.semanticscholar.org.
- 7.
CSO is available for download at https://w3id.org/cso/downloads.
- 8.
CSO Data Model - https://cso.kmi.open.ac.uk/schema/cso.
- 9.
SKOS Simple Knowledge Organization System - http://www.w3.org/2004/02/skos.
- 10.
Computer Science Ontology Portal - https://cso.kmi.open.ac.uk .
- 11.
Microsoft Academic Graph - https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/ .
- 12.
In particular, for the collocation analysis, we used min-count = 5 and threshold = 10.
- 13.
The final parameters of the word2vec model are: method = skipgram, embedding-size = 128, window-size = 10, min-count-cutoff = 10, max-iterations = 5.
- 14.
These three fields are well covered by CSO, which includes a total of 35 sub-topics for the Semantic Web, 173 for Natural Language Processing, and 396 for Data Mining.
- 15.
Medline dataset: https://www.nlm.nih.gov/bsd/medline.html .
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Salatino, A.A., Osborne, F., Thanapalasingam, T., Motta, E. (2019). The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_26
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