Urban Knowledge and Semantic Intelligence
A research direction bridging large language models and graph learning to give road networks generalizable urban semantics.
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Cross-Taxonomy Label Alignment in Transportation Networks With Prompt-Driven Large Language Models
LLM prompt-based pseudo-labels plus road-network structural embeddings align labels across transportation taxonomies, even with only 1% labeled data.
A Generalizable Semantic Graph Learning Framework for Road Network Representation
A heterogeneous graph framework (HetSemNet) that fuses LLM-derived POI semantics with road topology, generalizing across cities on road type classification.