Assigning scientific texts to existing ontologies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00643769" target="_blank" >RIV/67985807:_____/25:00643769 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.15439/2025F1850" target="_blank" >https://doi.org/10.15439/2025F1850</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15439/2025F1850" target="_blank" >10.15439/2025F1850</a>

Result language

angličtina

Original language name

Assigning scientific texts to existing ontologies

Original language description

Humans try to help computers understand the properties of the real world, and ontologies can be used for this task. Scientists publish their research in papers, and their results should be used to improve existing ontologies to be up-to-date. 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. A modification is based on the extension of one branch of the Siamese network to aggregate inputs from a group of embeddings. We have considered different embedders, in particular two variants of BERT, InferSent, GloVe with TF-IDF weighted mean, Doc2Vec in the distributed memory variant, and the Llama 3.1 with LLM2vec framework. Their quality has been evaluated on a use case with available ontologies from several application domains. The best results were achieved with InferSent and SentenceBERT.

Czech name

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Czech description

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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)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

ISBN

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ISSN

2300-5963

e-ISSN

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Number of pages

9

Pages from-to

185-193

Publisher name

IEEE

Place of publication

Piscataway

Event location

Krakow

Event date

Sep 14, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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