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

ArXi:2601.08841v2 Announce Type: replace 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.