## 3 Related Work  
A growing body of research has explored automating library classification assignment through machine learning and ontological approaches. More recently, large language models and conversational AI systems have emerged as promising tools for this task. Early work demonstrated that supervised learning models can assign UDC at the main classes level for scholarly texts, pointing to substantial time savings for librarians [3]. Hybrid and ontology-driven recommenders have further improved precision in domain-specific contexts, particularly in mathematics [4]. Simultaneously, the advent of LLM-powered conversational systems has spurred empirical studies on their effectiveness for traditional cataloguing tasks, revealing both promise and limitations in automated classification [5; 6] and subject-heading selection [7; 8; 9]. However, systematic comparative evaluations of multiple LLM architectures for UDC classification remain limited, despite the system's widespread use and unique hierarchical structure. The UDC is one of the most widely adopted international library classification schemes, rooted in the Dewey Decimal Classification system and continually updated to cover new subject areas. Its hierarchical decimal notation enables precise representation of document subjects, supporting faceted navigation and cross-lingual interoperability. Despite its strengths, manual UDC assignment is time-consuming and requires specialized expertise. In [3], a design-science approach was developed using classical machine learning models--trained on 70,000 librarian-classified scholarly texts--to automatically assign UDC to older, digitized documents. Their SVM-based classifier achieved over 90% correct assignment at hierarchical level 3, as corroborated by expert evaluation on 150 randomly selected texts. Building on classical approaches, recent work has explored deep learning methods. In [10], the authors fine-tuned a BERT transformer within KNIME to build a semi-automatic UDC recommender, reporting high AUC values though noting room for accuracy improvements when full UDC licensing is obtained. In [11], a hybrid model was proposed combining metadata-driven and collaborative filtering techniques tailored to Slovenian digital libraries, further demonstrating that mixed methods can boost recommendation hit rates in \begin{table}
\begin{tabular}{c l} \hline
**UDC** & **Interpretation** \\ \hline
51(075) & Math workbook \\ \hline
004.8:004.934(043.2) &
\begin{tabular}{l} Undergraduate thesis about an \\ artificial intelligence system \\ implementing automatic \\ speech recognition \\ \end{tabular} \\ \hline
338.48(410 London)“2021”(084.3)(0.034.2 PDF) &
\begin{tabular}{l} Tourist map of London from 2021 \\ in PDF format \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 3: Examples of simple and complex UDC expressions with interpretations. real-world cataloging scenarios. Collectively, these studies confirm that machine learning pipelines ranging from classical approaches to deep neural networks can effectively scale UDC assignment across large digital collections. Furthermore, ontology-driven methods offer semantic grounding that pure statistical models lack. In [4], the authors exploited the OntoMathPRO applied ontology to extract mathematical concepts (methods, problems, and equations) and build code maps for UDC codes in mathematics, enabling fine-grained recommendations with high relevance across closely related subdomains. Such knowledge-based recommenders model the classification decision process more explicitly, positioning them as valuable complements to data-driven classifiers, especially in domains with well-developed ontologies. The public release of accessible conversational systems like ChatGPT, Gemini, and Claude has catalyzed library-oriented evaluations [12].