ONTOLOGY BUILDING USING NATURAL LANGUAGE PROCESSING METHODS
DOI:
https://doi.org/10.24867/20BE12RokvicKeywords:
Ontology, Natural Language Processing, Neural Networks, Word EmbeddingsAbstract
Ontologies are widely used in numerous areas as a superior solution for domain knowledge representation. Traditional ontology building systems include many complicated and time-consuming tasks, but with the development of science, it is possible to automate the process, partially or entirely. This paper has a goal to represent the process of computer science ontology building using machine learning. The neural network was trained on university courses data and as a result word embeddings were used to build the ontology. The model was evaluated by comparing word embeddings with the ground truth.
References
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[3] Wohlgenannt, Gerhard, and Filip Minic. "Using word2vec to Build a Simple Ontology Learning System." International Semantic Web Conference (Posters & Demos). 2016.
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[5] https://ocw.mit.edu/index.htm.(приступљено у јуну 2022.)
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[2] Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).
[3] Wohlgenannt, Gerhard, and Filip Minic. "Using word2vec to Build a Simple Ontology Learning System." International Semantic Web Conference (Posters & Demos). 2016.
[4] Obeid, Charbel, et al. "Ontology-based recommender system in higher education." Companion Proceedings of the The Web Conference 2018. 2018.
[5] https://ocw.mit.edu/index.htm.(приступљено у јуну 2022.)
[6] vanRossum, Guido. "Python reference manual." Department of Computer Science [CS] R 9525 (1995).
[7] Noy, N. F., Crubézy, M., Fergerson, R. W., Knublauch, H., Tu, S. W., Vendetti, J., & Musen, M. A. (2003). Protégé-2000: an open-source ontology-development and knowledge-acquisition environment<. AMIA Symposium, (str. 953-953).
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Published
2022-11-05
Issue
Section
Electrotechnical and Computer Engineering
