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Computer Science Ontology
Automatically generated taxonomy of research topics in the field of Computer Science

The Computer Science Ontology (CSO) is an automatically generated taxonomy of research topics in the field of Computer Science. It was produced by the Open University in collaboration with Springer Nature by running an information extraction system over a large corpus of scientific articles. Several branches were manually improved by domain experts. The current version (CSO 3.2) includes about 14K research topics and 160K semantic relationships.

CSO is available in OWL, Turtle, and N-Triples. It is aligned with several other knowledge graphs, including DBpedia, Wikidata, YAGO, Freebase, and Cyc. New versions of CSO are regularly released on the CSO Portal.

CSO is mostly used to characterise scientific papers and other documents according to their research areas, in order to enable different kinds of analytics. The CSO Classifier is an open-source python tool for automatically annotating documents with CSO.

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Applications

See also

References

  1. Kotis, K.I., Vouros, G.A. and Spiliotopoulos, D., 2020. Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations. The Knowledge Engineering Review, 35. [1] https://spiliotopoulos.org/publications/Kotis%20et%20al.%20-%202020%20-%20Ontology%20engineering%20methodologies%20for%20the%20evolution%20of%20living%20and%20reused%20ontologies.pdf ↩

  2. Fathalla, S., Auer, S. and Lange, C., 2020, March. Towards the semantic formalization of science. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 2057-2059). [2] https://dl.acm.org/doi/pdf/10.1145/3341105.3374132 ↩

  3. "Press Release - Springer Nature". 5 January 2020. Retrieved 2020-01-10. https://group.springernature.com/it/group/media/press-releases/springer-nature-and-the-open-university-launch-a-unique/16386730 ↩

  4. "Last version of CSO". 6 July 2020. Retrieved 2020-07-06. https://cso.kmi.open.ac.uk/downloads ↩

  5. Salatino, A.A., Thanapalasingam, T., Mannocci, A., Birukou, A., Osborne, F. and Motta, E. (2019) The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas, Data Intelligence. [3] http://oro.open.ac.uk/66268/1/DataIntel-CSO.camera.ready.ORO.pdf ↩

  6. "The CSO Portal". Retrieved 4 January 2020. http://cso.kmi.open.ac.uk/ ↩

  7. Zhang, X., Chandrasegaran, S. and Ma, K.L., 2020. ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology. arXiv preprint arXiv:2003.05108. [4] https://arxiv.org/pdf/2003.05108.pdf ↩

  8. "The CSO Classifier". Retrieved 4 January 2020. https://pypi.org/project/cso-classifier/ ↩

  9. Iana, A., Jung, S., Naeser, P., Birukou, A., Hertling, S. and Paulheim, H., 2019, September. Building a conference recommender system based on SciGraph and WikiCFP. In International Conference on Semantic Systems (pp. 117-123). Springer, Cham.[5] http://suchanek.name/work/publications/www2007.pdf ↩

  10. Supriyati, E., Iqbal, M. and Khotimah, T., 2019. Using similarity degrees to improve fuzzy mining association rule based model for analysing IT entrepreneurial tendency. IIUM Engineering Journal, 20(2), pp.78-89. [6] https://journals.iium.edu.my/ejournal/index.php/iiumej/article/download/1096/714 ↩

  11. Borges, M.V.M., dos Reis, J.C. and Gribeler, G.P., 2019, June. Empirical Analysis of Semantic Metadata Extraction from Video Lecture Subtitles. In 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) (pp. 301-306). IEEE. [7] https://ieeexplore.ieee.org/document/8795409 ↩

  12. Zhang, X., Chandrasegaran, S. and Ma, K.L., 2020. ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology. arXiv preprint arXiv:2003.05108. [8] https://arxiv.org/pdf/2003.05108.pdf ↩