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| 研究生： |
翁子平 Weng, Zi-Ping |
|---|---|
| 論文名稱： |
基於注意力機制之高精確知識融合方法之設計-以酪農知識問答系統建置為例 Design of a High Accuracy Knowledge Fusion Scheme Based on Attention Mechanism–Implementation of Diary Knowledge QA System |
| 指導教授： |
龔旭陽
Kung, Hsu-Yang |
| 學位類別： |
碩士 Master |
| 系所名稱： |
管理學院 - 資訊管理系所 Department of Management Information Systems |
| 畢業學年度： | 109 |
| 語文別： | 中文 |
| 論文頁數： | 66 |
| 中文關鍵詞： | 知識問答系統 、知識圖譜 、注意力機制模型 、知識分歧 、知識融合 、知識抽取 、TF-IDF 、雙向門控循環單元 、條件隨機域 、酪農知識 |
| 外文關鍵詞： | Knowledge Question Answering, Attention Mechanism Model, Knowledge Divergence, Knowledge Fusion, Term frequency-Inverse document frequency, Bidirectional Gate Recurrent Unit, Conditional Random Field, Dairy Farmer Knowledge |
| DOI URL： | http://doi.org/10.6346/NPUST202100343 |
| 相關次數： | 點閱：24 下載：0 |
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摘 要:I
謝 誌:IX
圖目錄:XIV
表目錄:XVII
1. 緒論:1
1.1 研究背景與動機:1
1.2 研究目的與方法:3
1.3 論文架構:5
2. 文獻探討與技術背景:6
2.1 資料前處理:6
2.1.1 TF-IDF (Term frequency–Inverse document frequency):6
2.1.2 隱藏馬可夫模型(Hidden Markov Model, HMM):6
2.2 知識三元組:7
2.2.1 分佈式表示(Distributional Representation):7
2.2.2 詞嵌入(word embedding):8
2.2.3 資源描述框架(Resource Description Framework, RDF):9
2.2.4 命名實體識別(Named Entity Recognition, NER):9
2.2.5 雙向門控循環單元(Bidirectional Gated Recurrent Unit, Bi-GRU):10
2.2.6 條件隨機域(Conditional Random Field, CRF):11
2.3 知識建構(Knowledge Construction):12
2.3.1 三元組抽取:12
2.3.2 知識融合:13
2.3.3 注意力機制模型(Attention-based Model):14
2.4 知識檢索與知識問答:14
2.4.1 問句預處理:14
2.4.2 主要實體探索:15
2.4.3 Cypher:15
2.5 酪農產業現況:15
3. 研究方法與步驟:17
3.1 研究問題定義:17
3.2 系統架構:17
3.3 系統功能說明:19
3.4 知識抽取層:20
3.4.1 資料前處理:22
3.4.2 知識三元組:25
3.5 知識建構層:29
3.5.1 知識圖譜建置:31
3.5.2 知識驗證:33
3.6 知識檢索層:33
3.6.1 問句預處理:34
3.6.2 關聯檢索:35
3.7 知識表示層:37
3.7.1 候選答案建立:38
3.7.2 知識問答系統:40
4. 系統實作與效能評估:41
4.1 系統實作與效能評估:41
4.2 實驗硬體:41
4.3 開發軟體:41
4.4 資料集:42
4.5 知識抽取層:43
4.5.1 資料前處理:43
4.5.2 知識三元組:47
4.6 知識建構層:53
4.6.1 知識融合:53
4.6.2 注意力機制之知識融合:56
4.7 知識檢索層:57
4.7.1 問句預處理:57
4.7.2 關聯檢索:58
4.8 知識檢索層:59
4.8.1 候選答案排名:59
4.8.2 知識問答系統:61
5. 結論:63
6. 參考文獻:65
作者簡介:68
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校外公開2026/08/16