The LLMs4Subjects shared task invited system contributions that leverage a technical library’s tagged document corpus to learn document subject tagging, i.e., proposing adequate subjects given a document’s title and abstract. To address the imbalance of this training corpus, team LA2I2F devised a semantic retrieval-based system fusing the results of ontological and analogical reasoning in embedding vector space. Our results outperformed a naive baseline of prompting a llama 3.1-based model, whilst being computationally more efficient and competitive with the state of the art.
