H oma at S em E val-2025 Task 5: Aligning Librarian Records with O nto A ligner for Subject Tagging (2025)

Abstract: This paper presents our system, Homa, for SemEval-2025 Task 5: Subject Tagging, which focuses on automatically assigning subject labels to technical records from TIBKAT using the Gemeinsame Normdatei (GND) taxonomy. We leverage OntoAligner, a modular ontology alignment toolkit, to address this task by integrating retrieval-augmented generation (RAG) techniques. Our approach formulates the subject tagging problem as an alignment task, where records are matched to GND categories based on semantic similarity. We evaluate OntoAligner’s adaptability for subject indexing and analyze its effectiveness in handling multilingual records. Experimental results demonstrate the strengths and limitations of this method, highlighting the potential of alignment techniques for improving subject tagging in digital libraries.

## introduction libraries are the heart of every society and a cornerstone of education, serving as repositories of human knowledge and cultural heritage. as information landscapes evolve, these institutions must adapt to the growing volume and complexity of digital resources. therefore, technological innovation in both traditional libraries and modern digital library systems is essential to optimize workflows, enhance accessibility, and improve resource organization. with the rapid advancement of artificial intelligence (ai), particularly through large language models (llms) @xcite , there is an increasing need to integrate these technologies into library systems @xcite . llms offer capabilities in natural language understanding (nlu), knowledge retrieval, and automated categorization, making them valuable tools for subject tagging, metadata enrichment, and semantic search @xcite . by leveraging llms, libraries can enhance cataloging efficiency, improve interoperability with controlled vocabularies such as the gemeinsame normdatei (gnd) (german national library, 2025) librarian collections, and enable more precise and context-aware information retrieval. despite these advantages, integrating ai-driven solutions into library workflows presents challenges, including model interpretability, bias in automated tagging, and multilingual processing. addressing these issues requires developing robust frameworks that balance ai-powered automation with human oversight. llms4subjects @xcite is the first shared task of its kind organized within semeval-2025, challenging the research community to develop cutting-edge llmbased solutions for subject tagging of technical records from leibniz university's technical library (tibkat). the participants are tasked with leveraging llms to tag technical records using the gnd taxonomy. the bilingual nature of the task is designed to address the needs of library systems that often involve multi-lingual records. given these motivations, the llms4subjects shared task consist of the following two tasks: task 1 -learning the gnd taxonomy -incorporating the gnd subjects taxonomy, used by technische informationsbibliothek (tib) experts for indexing, into llms for subject tagging to enable llms to understand and utilize the taxonomy for subject classification effectively. task 2 -aligning subject tagging to tibkat -given a librarian record, a developed system should recommend gnd subjects based on semantic relationships in titles and abstracts. ontologies are a key building block for many applications in the semantic web. hence, ontology alignment, the process of identifying correspondences between entities in different ontologies, is a critical task in knowledge engineering. to this end, ontoaligner @xcite is a comprehensive modular and robust python toolkit for ontology alignment built to make ontology alignment easy to use for everyone. inspired by this vision, we adapted its technique for subject indexing, where we formulated a dataset into the input data structure of ontoaligner and used the retrieval-augmented generation (rag) technique to assess the ontoaligner capability in downstream tasks such as subject tagging. the experimental setting in this work plays a case study to analyze ontoaligner behavior toward how much it can be flexible and accurate for subject indexing and what are the bottlenecks. ## related work subject indexing in library systems has evolved to balance precision and adaptability, incorporating controlled vocabularies, social tagging, ontologybased indexing, and hybrid approaches. controlled vocabularies, such as the library of congress subject headings (lcsh), provide structured access to resources but require substantial intellectual effort to maintain consistency @xcite . while lcsh has expanded through cooperative contributions, it faces criticism for outdated terminology and limited flexibility @xcite . social tagging, introduced with web 2.0, allows users to generate metadata, enhancing discoverability and personalization @xcite . however, its effectiveness in library systems remains inconclusive, with studies suggesting that while tags aid browsing, they lack the specificity of controlled vocabularies @xcite . ontologybased indexing enhances retrieval accuracy by linking text to structured semantic concepts, addressing limitations of traditional keyword-based indexing @xcite . hybrid models integrating these approaches are increasingly advocated. tags can supplement subject headings rather than replace them @xcite , as seen in implementations like bibliocommons @xcite . however, usability challenges persist, particularly in supporting tag-based searches within catalog interfaces @xcite . this evolving landscape underscores the need for innovative indexing solutions that combine structured control with userdriven flexibility. ## methodology in this study, we employ the ontoaligner library @xcite -a retrieval-augmented generation (rag) pipeline -to align technical records from the tibkat for subject indexing tasks. this task involves generating relevant subject suggestions that accurately reflect the content of a given technical record. the rag pipeline is designed to handle multilingual, hierarchical data, ensuring that metadata and semantic relationships within the records are preserved for efficient retrieval. our proposed methodology consists of two main components: 1) ontoaligner pipeline, and 2) fine-tuning. ## limitation and conclusion the quantitative evaluation results in in this work, we explored ontoaligner as a case study for subject indexing, demonstrating its capability with minimal fine-tuning. the results highlight its effectiveness in aligning subjects, reinforcing its potential for real-world applications. however, further fine-tuning with additional computa-tional resources and data is necessary to enhance its precision and overall performance for the subject indexing task.