Open AccessTechnology of training a neural-network model for ontology population
Author(s) -
Павел Ломов,
AUTHOR_ID,
Marina Malozemova,
AUTHOR_ID
Publication year - 2021
Publication title -
trudy kolʹskogo naučnogo centra ran
Language(s) - English
Resource type - Journals
ISSN - 2307-5252
DOI - 10.37614/2307-5252.2021.5.12.016
Subject(s) - ontology , computer science , upper ontology , suggested upper merged ontology , process ontology , ontology based data integration , artificial neural network , population , artificial intelligence , domain (mathematical analysis) , ontology learning , natural language processing , extension (predicate logic) , machine learning , domain knowledge , programming language , mathematics , mathematical analysis , philosophy , demography , epistemology , sociology
The paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the structure of its classes and relations. A brief overview of existing ontology learning approaches is presented. A highly automated technology for ontology population based on training and application of the neural-network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.