Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts

0565961 - ÚI 2023 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Korel, L. - Behr, A. S. - Kockmann, N. - Holeňa, Martin
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts.
Proceedings of the 22st Conference Information Technologies – Applications and Theory (ITAT 2022). Aachen: Technical University & CreateSpace Independent Publishing, 2022 - (Ciencialová, L.; Holeňa, M.; Jajcay, R.; Jajcayová, R.; Mráz, F.; Pardubská, D.; Plátek, M.), s. 44-54. ISSN 1613-0073.
[ITAT 2022: Conference Information Technologies - Applications and Theory /22./. Zuberec (SK), 23.09.2022-27.09.2022]
Institucionální podpora: RVO:67985807
Klíčová slova: ontology * text data * text preprocessing * text representation learning * text classification
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ceur-ws.org/Vol-3226/paper5.pdf

This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks. The basic idea of the presented approach is to select a representative paragraph from a source text file, embed it to a vector space by a pre-trained fine-tuned transformer, and classify the embedded vector according to its relevance to a target ontology. We have considered different classifiers to categorize the output from the transformer, in particular random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and Gaussian process classifiers. Their suitability has been evaluated in a use case with ontologies and scientific texts concerning catalysis research. From results we can say the worst results have random forest. The best results in this task brought support vector machine classifier
Trvalý link: https://hdl.handle.net/11104/0337426