USING ONTOLOGIES IN TEXTS ANNOTATION

The work analyzes the text annotation methods used to work with ontologies. Annotation helps to structure information and simplifies its analysis. Two main approaches are considered: manual and automatic annotation. Manual annotation is more accurate, as it considers the context and language specifics, but is laborious and requires significant resources. Automatic annotation is faster, scales much faster, but can be prone to errors due to limited understanding of the context and a small amount of initial knowledge base. Tools and technologies for annotation are separately highlighted, software that automates this process. Problems associated with increasing the accuracy of automatic systems and integrating various data analysis methods are identified. Text annotation is important for the development of ontologies, automatic translation, and data analysis, which contributes to the improvement of technologies in these areas. Ontology helps solve the problem of incomplete information in the text, as well as identify and correct contradictions and inconsistencies.