Year: 2025
Type: conference-paper
Abstract: 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.
DOI: 10.15439/2025f1850
Authors: Lukáš Korel, Martin Holeňa
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G1055358128, G1299318184
OpenAlex ID: W4415420795