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Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis
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SYSNO ASEP 0568306 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis Author(s) Korel, L. (CZ)
Yorsh, U. (CZ)
Behr, A. S. (DE)
Kockmann, N. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDNumber of authors 5 Article number 14 Source Title Computers. - : MDPI - ISSN 2073-431X
Roč. 12, č. 1 (2023)Number of pages 25 s. Language eng - English Country CH - Switzerland Keywords text representation learning ; text classification ; text preprocessing ; text data ; ontology OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 000914556100001 EID SCOPUS 85146810459 DOI 10.3390/computers12010014 Annotation The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2024 Electronic address https://dx.doi.org/10.3390/computers12010014
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