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Text-to-Ontology Mapping via Natural Language Processing Models
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SYSNO ASEP 0565960 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Text-to-Ontology Mapping via Natural Language Processing Models Author(s) Yorsh, U. (CZ)
Behr, A. S. (DE)
Kockmann, N. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDNumber of authors 4 Source Title Proceedings of the 22st Conference Information Technologies – Applications and Theory (ITAT 2022). - Aachen : Technical University & CreateSpace Independent Publishing, 2022 / Ciencialová L. ; Holeňa M. ; Jajcay R. ; Jajcayová R. ; Mráz F. ; Pardubská D. ; Plátek M. - ISSN 1613-0073 Pages s. 28-34 Number of pages 7 s. Publication form Online - E Action ITAT 2022: Conference Information Technologies - Applications and Theory /22./ Event date 23.09.2022 - 27.09.2022 VEvent location Zuberec Country SK - Slovakia Event type EUR Language eng - English Country DE - Germany Keywords text analysis ; language models ; fastText ; BERT ; matching text to ontologies OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85139875540 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2023 Electronic address https://ceur-ws.org/Vol-3226/paper3.pdf
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