Text-to-Ontology Mapping via Natural Language Processing Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00565960" target="_blank" >RIV/67985807:_____/22:00565960 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-3226/paper3.pdf" target="_blank" >https://ceur-ws.org/Vol-3226/paper3.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Text-to-Ontology Mapping via Natural Language Processing Models

Popis výsledku v původním jazyce

The paper presents work in progress attempting to solve a text-to-ontology mapping problem. While ontologies are being created as formal specifications of shared conceptualizations of application domains, different users often create different ontologies to represent the same domain. For better reasoning about concepts in scientific papers, it is desired to pick the ontology which best matches concepts present in the input text. We have started to automatize this process and attack the problem by utilizing state-of-the-art NLP tools and neural networks. Given a specific set of ontologies, we experiment with different training pipelines for NLP machine learning models with the aim to construct representative embeddings for the text-to-ontology matching task. We assess the final result through visualizing the latent space and exploring the mappings between an input text and ontology classes.

Název v anglickém jazyce

Text-to-Ontology Mapping via Natural Language Processing Models

Popis výsledku anglicky

The paper presents work in progress attempting to solve a text-to-ontology mapping problem. While ontologies are being created as formal specifications of shared conceptualizations of application domains, different users often create different ontologies to represent the same domain. For better reasoning about concepts in scientific papers, it is desired to pick the ontology which best matches concepts present in the input text. We have started to automatize this process and attack the problem by utilizing state-of-the-art NLP tools and neural networks. Given a specific set of ontologies, we experiment with different training pipelines for NLP machine learning models with the aim to construct representative embeddings for the text-to-ontology matching task. We assess the final result through visualizing the latent space and exploring the mappings between an input text and ontology classes.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 22st Conference Information Technologies – Applications and Theory (ITAT 2022)

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

7

Strana od-do

28-34

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Zuberec

Datum konání akce

23. 9. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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