Text-to-Ontology Mapping via Natural Language Processing Models

0565960 - ÚI 2023 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Yorsh, U. - Behr, A. S. - Kockmann, N. - Holeňa, Martin
Text-to-Ontology Mapping via Natural Language Processing Models.
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.), s. 28-34. ISSN 1613-0073.
[ITAT 2022: Conference Information Technologies - Applications and Theory /22./. Zuberec (SK), 23.09.2022-27.09.2022]
Institucionální podpora: RVO:67985807
Klíčová slova: text analysis * language models * fastText * BERT * matching text to ontologies
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ceur-ws.org/Vol-3226/paper3.pdf

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.
Trvalý link: https://hdl.handle.net/11104/0337425