Retrieval-Augmented Generation (RAG) has revolutionized Large Language Models' ability to access external knowledge, but current graph-based RAG approaches face critical limitations in managing hierarchical knowledge: they impose rigid compression quotas per layer that damage local graph structures, and they focus primarily on topological structure while neglecting semantic coherence. We introduce T-Retriever, a novel framework that reformulates attributed graph retrieval as tree-based retrieval using a semantic and structure guided encoding tree. Our approach integrates two key innovations: (1) Adaptive Compression Encoding, which eliminates artificial layer-specific compression quotas in favor of a global optimization strategy that preserves the graph's natural hierarchical organization, and (2) Semantic-Structural Entropy (S²-Entropy), which jointly optimizes for both topological cohesion and semantic consistency when creating hierarchical partitions. Extensive experiments across diverse graph reasoning benchmarks demonstrate that T-Retriever significantly outperforms state-of-the-art RAG methods.