Triples and Knowledge-Infused Embeddings for Clustering and Classification of Scientific Documents

Abstract

The increasing volume and complexity of scientific literature demand robust methods for organizing and understanding research documents. In this study, we investigate whether structured knowledge, specifically, subject-predicate-object triples-improves clustering and classification of scientific papers. We present a modular pipeline that combines unsupervised clustering and supervised classification across four document representations: abstract, triples, abstract+triples, and hybrid. Using a filtered arXiv corpus, we evaluate four transformer embeddings (MiniLM, MPNet, SciBERT, SPECTER) with KMeans, GMM, and HDBSCAN, and then train downstream classifiers for subject prediction. Across a five-seed benchmark (seeds 40-44), abstract-only inputs provide the strongest and most stable classification performance, reaching 0.923 accuracy and 0.923 macro-F1 (mean). Triple-only and knowledge-infused variants do not consistently outperform this baseline. In clustering, KMeans/GMM generally outperform HDBSCAN on external validity metrics, while HDBSCAN exhibits higher noise sensitivity. We observe that adding extracted triples naively does not guarantee gains and can reduce performance depending on representation choice. These results refine the role of knowledge infusion in scientific document modeling: structured triples are informative but not universally beneficial, and their impact is strongly configuration-dependent. Our findings provide a reproducible benchmark and practical guidance for when knowledge-augmented representations help, and when strong text-only baselines remain preferable.