Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00565961" target="_blank" >RIV/67985807:_____/22:00565961 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/22:00359902
Result on the web
<a href="https://ceur-ws.org/Vol-3226/paper5.pdf" target="_blank" >https://ceur-ws.org/Vol-3226/paper5.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 22st Conference Information Technologies – Applications and Theory (ITAT 2022)
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
11
Pages from-to
44-54
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Zuberec
Event date
Sep 23, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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