The Computer Science Ontology (CSO) is an automatically generated taxonomy of research topics in the field of Computer Science.[1][2] 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.[3] Several branches were manually improved by domain experts. The current version (CSO 3.2[4]) includes about 14K research topics and 160K semantic relationships.[5]
CSO is mostly used to characterise scientific papers and other documents according to their research areas, in order to enable different kinds of analytics.[7] The CSO Classifier[8] is an open-source python tool for automatically annotating documents with CSO.
^ 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]
^ 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]
^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]
^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]
^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]
^ 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]
^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]
^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]
