Assigning scientific texts to existing ontologies

Humans try to help computers understand the properties of the real world, and ontologies can be used for this task.Manual enhancement of ontologies is highly time-consuming for domain experts.This paper proposes a solution to match a scientific text to the most relevant ontology using artificial neural networks.Our approach selects a paragraph or a sentence, uses representation learning to embed it into a vector space by some embedder, and measures its relevance to embedded textual properties from the selected ontology by a modified version of a Siamese neural network.We have considered different embedders, their quality has been evaluated on a use case with available ontologies from several application domains.