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.

翻译：本文探讨了如何使用人工神经网络寻找与科学文本最相关的本体的可能性。所提出方法的基本思路是：从源文本文件中选取一个代表性段落，通过预训练的微调Transformer将其嵌入向量空间，然后根据该嵌入向量与目标本体的相关性进行分类。我们考虑了不同的分类器来对Transformer的输出进行分类，特别是随机森林、支持向量机、多层感知器、k近邻和高斯过程分类器。在涉及催化学研究本体和科学文本的用例中评估了它们的适用性。从结果来看，随机森林的表现最差，而支持向量机分类器在此任务中表现最佳。