Text-to-Ontology Mapping via Natural Language Processing Models
The result's identifiers
Result code in 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>
Result on the web
<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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Alternative languages
Result language
angličtina
Original language name
Text-to-Ontology Mapping via Natural Language Processing Models
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
7
Pages from-to
28-34
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Zuberec
Event date
Sep 23, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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