隨著人工智慧發展， KG 已成為結構化知識管理的關鍵技術，然其建構過程耗時費力，且LLM雖能提升效率，卻面臨生成內容不穩定與產生幻覺等挑戰。現有自動化工具多缺乏互動性與即時修正能力，對非技術背景使用者門檻較高，為解決此問題，本研究設計並實現了一個名為「HCIKG」的大型語言模型增強人機協作知識圖譜建構系統。HCIKG 透過整合語音辨識、多輪對話引導，以及創新的 RAG 模組，將使用者的自然語言指令精準轉換為資料庫查詢語法，為驗證系統成效，本研究透過四階段實驗，證實HCIKG在系統易用性與人機協作效能上，均顯著優於傳統工具，其核心的RAG提示策略，亦在準確率與運算成本間取得最佳平衡，最終，產出的高品質知識圖譜更能成功應用於自動化考題生成等下游任務，完整展現了本框架的有效性。;The construction of Knowledge Graphs (KGs), a key technology in structured knowledge management, presents a significant challenge due to its time-consuming and labor-intensive nature. While Large Language Models (LLMs) can enhance efficiency, they grapple with issues of instability and hallucination in content generation. Existing automated tools often lack interactivity and real-time correction capabilities, posing a high technical barrier for non-technical users. To address these issues, this study designs and implements an LLM-Enhanced Human-AI Collaborative Knowledge Graph Construction system, named "HCIKG". HCIKG integrates speech recognition, multi-turn dialogue guidance, and an innovative Retrieval-Augmented Generation (RAG) module to precisely convert users′ natural language commands into database query syntax. A four-stage experiment was conducted to validate the system′s efficacy. The results indicate that HCIKG significantly outperforms traditional tools in system usability and collaborative performance. Furthermore, its core RAG-based strategy strikes an optimal balance between accuracy and computational cost. The practical utility of the framework is demonstrated by the successful application of the resulting high-quality knowledge graph in downstream tasks, such as automated exam question generation, thus confirming the framework′s overall effectiveness.
