請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林仲彥 | zh_TW |
| dc.contributor.advisor | Chung-Yen Lin | en |
| dc.contributor.author | 葉政翔 | zh_TW |
| dc.contributor.author | Zheng-Xiang Ye | en |
| dc.date.accessioned | 2025-07-02T16:24:10Z | - |
| dc.date.available | 2025-07-03 | - |
| dc.date.copyright | 2025-07-02 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-06-20 | - |
| dc.identifier.citation | Alm, E. and Arkin, A. P. 2003. Biological networks. Current opinion in structural biology 13, 2, 193-202.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97546 | - |
| dc.description.abstract | 生物資料庫作為實驗研究與文獻彙整的核心樞紐，使科學家能高效的存取其專業領域內的資訊。資料庫提供者的目標不僅在於收集高品質的數據，亦在於確保服務的穩定性及搜尋結果的準確性。近年來大型語言模型的突破賦予模型強大的語意理解能力，使直覺式的自然語言搜尋成為可能。本研究整合大型語言模型至兩個不同的生物資料庫。MSCare是一個基於間質幹細胞PubMed文獻所建構的聊天機器人，為與非結構化文字資料互動的例子；TWHM聊天機器人則輔助臺灣漢醫藥 (TWHM) 資料庫，為與關聯式資料庫中結構化資料互動的例子。MSCare利用文字嵌入 (text embeddings) 與知識圖譜 (knowledge graph) 擷取相關文獻資訊並進行推理；TWHM聊天機器人則利用大型語言模型生成SQL，以支援藉自然語言進行進階資料庫查詢的技術。本研究設計了客製化的評估方法，用以分析並提升兩個系統的回應品質。結果顯示，MSCare在超過75%的問題上優於基準的大型語言模型，該表現主要來自於文字嵌入方法的貢獻。知識圖譜進一步提升了回應多樣性，並支援間接關係的推理，儘管在回應完整性方面仍有部分限制。MSCare的知識圖譜呈現無尺度網路 (scale-free network) 的特性，並有效捕捉MSC研究中的生物實體。藉本研究設計之資料表選擇與查詢優化策略，TWHM聊天機器人在SQL生成與執行方面有高成功率。本研究驗證了整合大型語言模型至生物資料庫的可行性。然而，在知識圖譜建構、檢索策略及系統效能的評估上仍存在挑戰，為後續研究與優化的重要方向。 | zh_TW |
| dc.description.abstract | Biological databases serve as central hubs for collecting and organizing experimental research and literature, enabling scientists to efficiently access domain-specific information. Database providers aim not only to curate high-quality data but also to ensure stable services and accurate search results. Recent advances in large language models (LLMs) have introduced powerful semantic understanding capabilities, allowing for more intuitive searches using natural language. This study explores the integration of LLMs into two distinct biological databases. MSCare, a chatbot built on PubMed articles related to mesenchymal stem cells (MSCs), enables interaction with unstructured textual data. The TWHM chatbot, developed to supplement the Taiwan Han Medicine (TWHM) database, facilitates interaction with structured data stored in a relational database. MSCare integrates text embeddings and a knowledge graph to extract biomedical content and support reasoning, while the TWHM chatbot uses LLM-based SQL query generation to support advanced searches based on natural language questions. Custom evaluation methods were developed to assess and enhance the response quality of both systems. Results show that MSCare outperforms a baseline LLM on more than 75% of questions, with the primary contribution coming from the text embedding approach. The knowledge graph further enhances response diversity and supports reasoning over indirect relationships, despite some limitations in contextual completeness. The MSC knowledge graph exhibits scale-free properties and effectively captures key entities central to MSC research. The TWHM chatbot achieves a high success rate in SQL query generation and execution, enabled by tailored schema selection and query refinement strategies. This study demonstrates the feasibility of integrating LLMs into biological databases. Nevertheless, challenges remain in knowledge graph construction, retrieval strategy design, and precise system performance evaluation. These areas represent key directions for future enhancement. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-02T16:24:10Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-02T16:24:10Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii Contents v List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Aims and Objectives 3 1.3 Thesis Structure 4 Chapter 2 Background 5 2.1 Language Models 5 2.1.1 General Concepts 5 2.1.2 A Brief History of Language Models 6 2.1.3 Capabilities of Large Language Models 7 2.2 Retrieval-Augmented Generation (RAG) 8 2.3 Database Management System (DBMS) 9 2.4 Text Embedding Models and Embedding-Based RAG 11 2.4.1 Text Embedding Models 11 2.4.2 RAG with Text Embedding Models 12 2.5 Knowledge Graph and Graph RAG 14 2.5.1 Biomedical Knowledge Graph 14 2.5.2 RAG with Knowledge Graphs 15 2.6 Text-to-SQL 17 Chapter 3 Materials and Methods 19 3.1 Chatbot System Architecture 19 3.1.1 Overview 19 3.1.2 Chat History Management 21 3.1.3 Language Detection 21 3.1.4 Data Retrieval and Response Generation 21 3.1.5 Implementation Details 22 3.2 MSCare Design and Evaluation 22 3.2.1 MSCare Data Sources 24 3.2.2 MSCare Text Embedding Construction and Retrieval 24 3.2.3 MSCare Knowledge Graph Construction 24 3.2.4 MSCare Knowledge Graph Retrieval 27 3.2.5 MSCare Response Generation 30 3.2.6 MSCare Evaluation 30 3.3 TWHM Chatbot Design and Evaluation 36 3.3.1 TWHM Data Source 38 3.3.2 TWHM Data Storage 39 3.3.3 TWHM Data Retrieval 41 3.3.4 TWHM Chatbot Response Generation 42 3.3.5 TWHM Chatbot Evaluation 42 Chapter 4 MSCare Results 46 4.1 Text Embedding-Based Retrieval 46 4.1.1 Text Chunk Statistics 46 4.1.2 Text Embedding Retrieval Evaluation 47 4.2 MSC Knowledge Graph 50 4.2.1 Graph Statistics 50 4.2.2 Graph Entity Connectivity and Topology 52 4.2.3 Knowledge Graph Retrieval Evaluation 54 4.2.4 Reasoning with Paths in the Knowledge Graph 56 4.3 MSCare Response Quality Evaluation 58 4.4 MSCare Response Case Study 61 Chapter 5 TWHM Chatbot Results 70 5.1 TWHM Database Statistics 70 5.2 Text-to-SQL Evaluation 70 5.2.1 Number of Generated Queries per Question 70 5.2.2 Refiner Invocation Frequency 73 5.2.3 Query Quality Evaluation 75 5.3 TWHM Chatbot Response Case Study 79 Chapter 6 Discussion 83 6.1 Text Embedding 83 6.1.1 Relevance of Retrieved Text Chunks 83 6.1.2 Text Chunking Strategies and Chunk Size 84 6.2 Knowledge Graph 84 6.2.1 MSC Knowledge Graph 84 6.2.2 Graph Construction 85 6.2.3 Graph Retrieval Strategies 86 6.2.4 Indirect Relationship Inference 87 6.2.5 Response Presentation 88 6.3 Text-to-SQL 88 6.3.1 Database Schema Selection and Representation 89 6.3.2 SQL Quality Dependency on the Choices of LLMs 90 6.3.3 Extensibility of the Text-to-SQL Approach Developed in this Study 90 6.4 Limitations 91 6.4.1 Response Generation 91 6.4.2 Evaluation Methods 92 Chapter 7 Conclusion 94 Appendices 95 References 135 | - |
| dc.language.iso | en | - |
| dc.subject | 大型語言模型 | zh_TW |
| dc.subject | 自然語言轉SQL | zh_TW |
| dc.subject | 知識圖譜 | zh_TW |
| dc.subject | 語意搜尋 | zh_TW |
| dc.subject | 檢索增強生成 | zh_TW |
| dc.subject | 生物資料庫 | zh_TW |
| dc.subject | semantic search | en |
| dc.subject | retrieval-augmented generation | en |
| dc.subject | large language models | en |
| dc.subject | biological databases | en |
| dc.subject | text-to-SQL | en |
| dc.subject | knowledge graph | en |
| dc.title | 與生醫資料庫對話：透過自然語言轉SQL、文字嵌入及知識圖譜方法探索檢索增強生成的應用 | zh_TW |
| dc.title | Communicating with Biomedical Databases: Exploring Retrieval-Augmented Generation via Text-to-SQL, Text Embedding, and Knowledge Graph-Based Approaches | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 莊樹諄;黃瀚萱;林泰元;陳淑華 | zh_TW |
| dc.contributor.oralexamcommittee | Trees-Juen Chuang;Hen-Hsen Huang;Thai-Yen Ling;Shu-Hwa Chen | en |
| dc.subject.keyword | 生物資料庫,大型語言模型,檢索增強生成,語意搜尋,知識圖譜,自然語言轉SQL, | zh_TW |
| dc.subject.keyword | biological databases,large language models,retrieval-augmented generation,semantic search,knowledge graph,text-to-SQL, | en |
| dc.relation.page | 142 | - |
| dc.identifier.doi | 10.6342/NTU202501236 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-06-20 | - |
| dc.contributor.author-college | 生命科學院 | - |
| dc.contributor.author-dept | 基因體與系統生物學學位學程 | - |
| dc.date.embargo-lift | 2025-07-03 | - |
| 顯示於系所單位： | 基因體與系統生物學學位學程 | |
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