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

• URL: http://arxiv.org/abs/2309.09203v1
• Date: Sun, 17 Sep 2023 08:08:50 GMT
• Title: Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts
• Authors: Luk\'a\v{s} Korel, Alexander S. Behr, Norbert Kockmann and Martin Hole\v{n}a
• Abstract summary: This paper provides an insight into the possibility of how to find most relevant texts using artificial networks. The basic idea of presented approach is to select a representative from a source text file and embed it to a vector space. We have considered different classifiers to categorize the embedded output from the transformer, in particular a random forest.
• Score: 44.99833362998488
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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