AI RESEARCH
Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning
arXiv CS.LG
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ArXi:2603.08159v1 Announce Type: new Hierarchical knowledge structures are ubiquitous across real-world domains and play a vital role in organizing information from coarse to fine semantic levels. While such structures have been widely used in taxonomy systems, biomedical ontologies, and retrieval-augmented generation, their potential remains underexplored in the context of Text-Rich Networks (TRNs), where each node contains rich textual content and edges encode semantic relationships.