DESIGN AND DEVELOPMENT OF INSTRUMENTAL TOOLS FOR SEMANTIC ANALYSIS OF BIG DATA SCIENTIFIC AND TECHNOLOGICAL SOLUTIONS IN THE FIELD OF ENERGY
Bibliographic record
Abstract
В статье рассмотрены подходы к проектированию и реализации отдельных компонентов инструментальных средств для семантического анализа извлекаемой из открытых источников информации о научных и технологических решениях в области энергетики. Рассмотрена структура билингвистической онтологии, позволяющая решать задачу классификации информации с учётом ее представления в различных языках и синонимии. Рассмотрен подход к поиску и обработке информации из открытых источников, основанный на применении разработанных авторами средств семантического анализа, реализация которых выполнялась на Python с использованием библиотеки Natural Language Toolkit. The article discusses approaches to the design and implementation of individual components of instrumental tools for semantic analysis of information on scientific and technological solutions in the field of energy. This information has already been placed open sources. The structure of billinguistic ontology is considered, which makes it possible to solve the task of classifying information, taking into account its submission in various languages and synonyms. The authors reviewed the approach to the search and processing of information from open sources based on the use of semantic analysis developed by authors, the implementation of which was performed on Python using the Natural Language Toolkit library
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".