A Probabilistic Method for Hierarchical Multisubject Classification of Documents Based on Multilingual Subject Term Vocabularies

• Published in: IEEE open journal of the Computer Society Vol. 6; pp. 1294 - 1305
• Main Authors: Makris, Nikolaos, Koutsileou, Stamatina K., Mitrou, Nikolaos
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Academic textbooks; Accuracy; Bayes methods; Bayesian analysis; Classification; classification algorithms; controlled vocabularies; Decision trees; digital libraries; document relevance; Effectiveness; hierarchical multisubject classification; HMC; Information retrieval; Internet; KALLIPOS project; Machine learning; Multi label classification; Natural language processing; Probabilistic logic; Probabilistic methods; Probability distribution; Real time; Springer ebooks; Statistical inference; statistical natural language processing; subject headings; Taxonomy; Textbooks; Training; User interfaces; Vectors; Vocabulary
• ISSN: 2644-1268; 2644-1268
• DOI: 10.1109/OJCS.2025.3592254

Hierarchical Multilabel Classification (HMC) is a challenging task in information retrieval, especially within scientific textbooks, where the objective is to allocate multiple labels adhering to a hierarchical taxonomy. This research presents a new language neutral methodology for HMC to assess documents as normalised weighted distributions of well-defined subjects across hierarchical levels, based on a hierarchical subject term vocabulary. The proposed approach utilizes Bayesian formulas, in contrast to typical methods that depend on machine learning models, thereby obviating the necessity for resource-intensive training processes at various hierarchical levels. The method integrates refined pre-processing techniques, such as natural language processing (NLP) and filtering of non-distinctive terms, to enhance classification accuracy. It employs Bayesian inference along with real time and cached computations across all hierarchical levels, yielding an effective, time-efficient and interpretable classification method while ensuring scalability for large datasets. Experimental results demonstrate the potency of the algorithm to classify scientific textbooks across hierarchical subject tiers with significant precision and recall and retrieve semantically related scientific textbooks, thereby verifying its efficacy in tasks requiring hierarchical subject classification. This study presents a streamlined, interpretable alternative to model-dependent HMC approaches, rendering it particularly appropriate for real-world applications in educational and scientific fields. Furthermore, in the context of the present study, two public Web User Interfaces were published, the first is founded on Skosmos to illustrate the hierarchical structure of the subject term vocabulary, while the second one employs the HMC method to present in real-time the classification between subjects in English and Greek textual data.