Objectives. The work considers whether a semantic-ontological model for scientific text analysis can support practical tools for establishing information morphism. Using VAK6 specialty passports as the textual ontological basis, we propose a graph-based model that reconstructs a proximity profile to specialty codes from an article or dissertation abstract to map the document space to the passport space.

Methods. Processing the passports as a single corpus, a shared unigram and bigram vocabulary is constructed from their chunks. Term frequency is computed in the form of inverse document frequency (TF-IDF) representations to construct local semantic graphs on the basis of incremental construction of an associative network (ICAN). For each document passport pair, similarity measures are merged into a hybrid metric by aggregation within lexical and semantic layers. Scores are converted into a probability distribution via codes based on temperature softmax functions. The model is evaluated on a corpus of dissertation abstracts and a corpus of articles of VAK list journals7, and the results are compared with large language models.

Results. The hybrid scheme, which achieves average top 1 accuracy of about 0.69 and top 3 of about 0.90 on abstracts, reaches 0.91 and 0.93 on articles to outperform lexical-only and semantic-only variants. Considered relative to large language models, the hybrid scheme achieves superior top 1 accuracy for articles and comparable accuracy in top 3, while remaining interpretable through n grams and contextual passport graphs.

Conclusions. The proposed model, which uses VAK passports to provide a practical ontological foundation, represents an interpretable and computationally efficient alternative for code selection and thematic profiling that accounts for interdisciplinarity.