arXiv Computation and Language October 5, 2026 By Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

Read the original on arXiv Computation and Language →

Enrich-on-Graph (EoG) is a flexible framework that uses large language models to enrich knowledge graphs, thereby bridging the semantic gap between structured graphs and unstructured queries in complex reasoning tasks. By leveraging LLMs’ prior knowledge, EoG enables efficient evidence extraction from knowledge graphs, achieving precise and robust reasoning while maintaining low computational costs and scalability. The authors also introduce three graph quality evaluation metrics for query‑graph alignment, theoretically validate their optimization objectives, and demonstrate state‑of‑the‑art performance on two KGQA benchmark datasets.

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