Assigning scientific texts to existing ontologies

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Assigning scientific texts to existing ontologies

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Assigning scientific texts to existing ontologies

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

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ISSN

2300-5963

e-ISSN

—

Počet stran výsledku

9

Strana od-do

185-193

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Krakow

Datum konání akce

14. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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