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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏志平(Chih-Ping Wei) | |
| dc.contributor.author | Yu-Chen Her | en |
| dc.contributor.author | 何昱辰 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:06:58Z | - |
| dc.date.available | 2023-08-16 | |
| dc.date.copyright | 2021-11-02 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-13 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81935 | - |
| dc.description.abstract | 知識圖譜驗證，或者稱作知識圖譜清理，是一項辨識圖譜中的實體間關係是否正確的任務，除了能夠有效地改善圖譜的品質之外，經過清理後的知識圖譜亦能提升其延伸應用的表現。以生醫領域為例，若能去除不正確的關係，將能協助進行舊藥新用等其他應用。 過往有些許研究致力於知識圖譜驗證的發展，例如在2019年Wang等學者提出以功能相依等規則作為度量衡，藉由計算移除特定資料前後的度量衡差距來判斷是否應該移除這些特定資料。另外2020年Ge等學者的研究則是先使用圖神經網路技術從乾淨的知識圖譜中萃取出各個實體及關係的嵌入向量，再利用嵌入向量來訓練一個能夠判斷關係正確性的分類模型。儘管上述方法均有不錯的表現，但仍存在一些限制。首先，制定當作衡量標準的規則是一項費時且需要相關領域知識的工作，而使用嵌入向量則會有未登錄詞的問題。此外，若方法需仰賴額外的乾淨知識圖譜，則會增加該方法的不可行性。 在這項研究中，我們認為能夠使用兩個實體間所有關係的資訊來判斷一個關係是否正確，並且根據此想法提出特徵工程方法、深度學習方法、以及兩者混合後的方法來驗證生醫知識圖譜。根據實驗結果，我們發現實體間的其他關係確實能幫助判斷關係的正確性。此外混合後的方法在準確率、精準度、召回率以及F1分數都取得了更好的成績，表示設計良好的特徵能夠有效地提升深度學習模型的表現。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:06:58Z (GMT). No. of bitstreams: 1 U0001-0809202100134000.pdf: 2843688 bytes, checksum: 0adf4abdbadc51af82c3cfa46652c786 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii Table of Contents v List of Figures viii List of Tables ix Chapter 1 Introduction 1 1.1. Background 1 1.2. Research Motivation 5 1.3. Research Objective 7 Chapter 2 Literature Review 9 2.1. Biomedical Literature Resource 9 2.2. Knowledge Graph Cleaning 13 2.2.1. Knowledge Graph 13 2.2.2. Rule-based Methods 14 2.2.3. Knowledge Graph-based Methods 17 Chapter 3 Our Proposed Methods 21 3.1. Term Definition and Problem Statement 21 3.1.1. Term Definition 21 3.1.2. Problem Statement 24 3.2. Data Collection 24 3.3. Knowledge Graph Verification Methods 27 3.3.1. BI (Bibliographic Information) Method 28 3.3.2. SA (Sibling Attention) Method 34 3.3.3. SABI (Sibling Attention with Bibliographic Information) Method 42 3.4. Objective Function 44 3.5. Training Strategies 44 Chapter 4 Experiments 47 4.1. Dataset and Evaluation Metrics 47 4.2. Experimental Settings 50 4.3. Performance Comparison 52 4.4. Detailed Analysis of the Proposed BI Method 54 4.5. Detailed Analysis of the Proposed SA Method 56 4.5.1. Effects of BioBERT Fine-tuning Strategy 57 4.5.2. Effects of Use of Residual Block 58 4.5.3. Effects of Use of Feature Concatenation Layer 59 4.6. Performance on Different Category Sets 60 Chapter 5 Conclusion 64 5.1. Contributions 64 5.2. Future Works 65 References 67 | |
| dc.language.iso | en | |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 預訓練語言模型 | zh_TW |
| dc.subject | 特徵工程 | zh_TW |
| dc.subject | 生醫知識圖譜驗證 | zh_TW |
| dc.subject | 知識圖譜驗證 | zh_TW |
| dc.subject | 自我注意力機制 | zh_TW |
| dc.subject | Pretrained language model | en |
| dc.subject | Feature engineering | en |
| dc.subject | Biomedical knowledge graph verification | en |
| dc.subject | Self-attention mechanism | en |
| dc.subject | Knowledge graph verification | en |
| dc.subject | Deep learning | en |
| dc.title | 知識圖譜驗證：特徵工程與深度學習方法 | zh_TW |
| dc.title | Knowledge Graph Verification: Feature Engineering vs. Deep Learning Methods | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.advisor-orcid | 魏志平(0000-0003-4150-3926) | |
| dc.contributor.oralexamcommittee | 楊錦生(Hsin-Tsai Liu),吳家齊(Chih-Yang Tseng) | |
| dc.subject.keyword | 知識圖譜驗證,生醫知識圖譜驗證,特徵工程,深度學習,預訓練語言模型,自我注意力機制, | zh_TW |
| dc.subject.keyword | Knowledge graph verification,Biomedical knowledge graph verification,Feature engineering,Deep learning,Pretrained language model,Self-attention mechanism, | en |
| dc.relation.page | 73 | |
| dc.identifier.doi | 10.6342/NTU202103048 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-09-14 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-08-16 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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