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METHODOLOGY FOR POPULATING A DIGITAL OBJECT MODEL FROM HETEROGENEOUS INFORMATION RESOURCES BASED ON QUANTITATIVE ASSESSMENT OF DATA EXTRACTION METHODS EFFICIENCY

Artamonov Aleksey A.  (PhD, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia)

The increasing volume of unstructured scientific data requires the development of effective methodologies for automated extraction and structuring of information. The relevance of the study is determined by the need to create holistic models of digital objects for subsequent comprehensive analysis under conditions of data source heterogeneity. The problem lies in the absence of a quantitatively substantiated methodology for selecting optimal approaches to data extraction from various types of documents while ensuring required reliability of results. The purpose of this work is to develop and verify a methodology for populating a digital object model from heterogeneous information resources with quantitative assessment of the effectiveness of applied data extraction methods using scientific publications as an example.

Keywords:data extraction, digital object, scientific publications, NLP, reliability assessment, NoSQL, document-oriented databases
 

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Citation link:
Artamonov A. A. METHODOLOGY FOR POPULATING A DIGITAL OBJECT MODEL FROM HETEROGENEOUS INFORMATION RESOURCES BASED ON QUANTITATIVE ASSESSMENT OF DATA EXTRACTION METHODS EFFICIENCY // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2026. -№01. -С. 45-50 DOI 10.37882/2223-2966.2026.01.05
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