Temporal Knowledge Graphs (TKGs) represent dynamic knowledge structures that encode time-sensitive relationships between entities, enabling systems to understand how facts evolve over time. Recent approaches to Temporal Knowledge Graph Reasoning (TKGR) have leveraged Large Language Models (LLMs), but face significant limitations: they often rely solely on first-order historical information, struggle with heavy information loads, and have yet to fully utilize LLMs’ potential for reasoning with semantically similar information. Additionally, current methods either lack interpretability or struggle with effective temporal rule learning. We present MSKGen (Multi-Source Knowledge-Based Generation), a novel query-aware approach for TKGR that integrates multiple knowledge sources to generate accurate predictions. By integrating rule-based facts with semantically retrieved facts, MSKGen maintains interpretability while maximizing LLMs’ semantic capabilities, addressing the information load challenges faced by current LLM implementations and offering significant advancements in combining structured temporal reasoning with semantic understanding for knowledge graph reasoning tasks. Experimental results across several common datasets demonstrate MSKGen’s superior performance, achieving significant improvements over state-of-the-art methods, confirming the effectiveness of our multi-source knowledge integration approach for temporal knowledge graph reasoning tasks.