High-Information-Density Knowledge Graph Retrieval Augmentation for Large Language Models

Retrieval-Augmented Generation (RAG) mitigates hallucination and knowledge staleness in large language models (LLMs) by injecting external knowledge into the context window, yet its effectiveness is rigidly constrained by attention dilution and inference cost. Existing knowledge-graph-enhanced methods follow a sequential “retrieve-then-compress” paradigm in which retrieval and compression are optimized independently, preventing the compressor from rectifying redundancy and noise introduced during retrieval and making it difficult to achieve globally efficient information allocation under a limited token budget. To address this paradigm-level limitation, we propose LOKG (LLM-Oriented Knowledge Graph), a high-information-density knowledge graph retrieval augmentation framework that uses token-level information density maximization as a shared design objective across schema compression, subgraph retrieval, and triple encoding. LOKG comprises three synergistic modules: Semantic Compression of Ontology Schema (SCOS) offline merges semantically overlapping relations by fusing normalized mutual information, Jaccard co-occurrence, and TransE embedding similarity under a conditional-entropy-based adaptive threshold; Topology-Aware Indexing and Multi-granularity Retrieval (TAIMR) computes topology-aware multi-hop path scores using relation-aware graph attention and node information gain, then applies late query–answer semantic re-ranking in the E5 space; Information-Theoretic Triple Compression (ITTC) formulates granularity assignment as a multiple-choice knapsack problem, adaptively allocating compact, balanced, or verbose representations to candidate triples to maximize injected information under the budget constraint. Experiments on four KGQA evaluation settings from three benchmark families (CWQ, WebQSP, PathQuestion-2hop, and PathQuestion-3hop) demonstrate that under a single-LLM-call constraint, LOKG achieves a superior accuracy–efficiency trade-off. Ablation studies further verify the orthogonality and synergy of the three modules. LOKG offers a transparent, auditable, and low-cost solution for knowledge graph retrieval-augmented generation under token budget constraints.